TestLSTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM\n",
    "\n",
    "#### I want to se how LSTM performs on a large dataset predicting just a day ahead\n",
    "###### Using the Time Series: \"con no1 mwh/h cet min15 a\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with con no1 mwh/h cet min15 a\n",
      "Working with tt no1 con ec00 °c cet min15 f\n",
      "Done 1 out of 366\n",
      "Done 2 out of 366\n",
      "Done 3 out of 366\n",
      "Done 4 out of 366\n",
      "Done 5 out of 366\n",
      "Done 6 out of 366\n",
      "Done 7 out of 366\n",
      "Done 8 out of 366\n",
      "Done 9 out of 366\n",
      "Done 10 out of 366\n",
      "Done 11 out of 366\n",
      "Done 12 out of 366\n",
      "Done 13 out of 366\n",
      "Done 14 out of 366\n",
      "Done 15 out of 366\n",
      "Done 16 out of 366\n",
      "Done 17 out of 366\n",
      "Done 18 out of 366\n",
      "Done 19 out of 366\n",
      "Done 20 out of 366\n",
      "Done 21 out of 366\n",
      "Done 22 out of 366\n",
      "Done 23 out of 366\n",
      "Done 24 out of 366\n",
      "Done 25 out of 366\n",
      "Done 26 out of 366\n",
      "Done 27 out of 366\n",
      "Done 28 out of 366\n",
      "Done 29 out of 366\n",
      "Done 30 out of 366\n",
      "Done 31 out of 366\n",
      "Done 32 out of 366\n",
      "Done 33 out of 366\n",
      "Done 34 out of 366\n",
      "Done 35 out of 366\n",
      "Done 36 out of 366\n",
      "Done 37 out of 366\n",
      "Done 38 out of 366\n",
      "Done 39 out of 366\n",
      "Done 40 out of 366\n",
      "Done 41 out of 366\n",
      "Done 42 out of 366\n",
      "Done 43 out of 366\n",
      "Done 44 out of 366\n",
      "Done 45 out of 366\n",
      "Done 46 out of 366\n",
      "Done 47 out of 366\n",
      "Done 48 out of 366\n",
      "Done 49 out of 366\n",
      "Done 50 out of 366\n",
      "Done 51 out of 366\n",
      "Done 52 out of 366\n",
      "Done 53 out of 366\n",
      "Done 54 out of 366\n",
      "Done 55 out of 366\n",
      "Done 56 out of 366\n",
      "Done 57 out of 366\n",
      "Done 58 out of 366\n",
      "Done 59 out of 366\n",
      "Done 60 out of 366\n",
      "Done 61 out of 366\n",
      "Done 62 out of 366\n",
      "Done 63 out of 366\n",
      "Done 64 out of 366\n",
      "Done 65 out of 366\n",
      "Done 66 out of 366\n",
      "Done 67 out of 366\n",
      "Done 68 out of 366\n",
      "Done 69 out of 366\n",
      "Done 70 out of 366\n",
      "Done 71 out of 366\n",
      "Done 72 out of 366\n",
      "Done 73 out of 366\n",
      "Done 74 out of 366\n",
      "Done 75 out of 366\n",
      "Done 76 out of 366\n",
      "Done 77 out of 366\n",
      "Done 78 out of 366\n",
      "Done 79 out of 366\n",
      "Done 80 out of 366\n",
      "Done 81 out of 366\n",
      "Done 82 out of 366\n",
      "Done 83 out of 366\n",
      "Done 84 out of 366\n",
      "Done 85 out of 366\n",
      "Done 86 out of 366\n",
      "Done 87 out of 366\n",
      "Done 88 out of 366\n",
      "Done 89 out of 366\n",
      "Done 90 out of 366\n",
      "Done 91 out of 366\n",
      "Done 92 out of 366\n",
      "Done 93 out of 366\n",
      "Done 94 out of 366\n",
      "Done 95 out of 366\n",
      "Done 96 out of 366\n",
      "Done 97 out of 366\n",
      "Done 98 out of 366\n",
      "Done 99 out of 366\n",
      "Done 100 out of 366\n",
      "Done 101 out of 366\n",
      "Done 102 out of 366\n",
      "Done 103 out of 366\n",
      "Done 104 out of 366\n",
      "Done 105 out of 366\n",
      "Done 106 out of 366\n",
      "Done 107 out of 366\n",
      "Done 108 out of 366\n",
      "Done 109 out of 366\n",
      "Done 110 out of 366\n",
      "Done 111 out of 366\n",
      "Done 112 out of 366\n",
      "Done 113 out of 366\n",
      "Done 114 out of 366\n",
      "Done 115 out of 366\n",
      "Done 116 out of 366\n",
      "Done 117 out of 366\n",
      "Done 118 out of 366\n",
      "Done 119 out of 366\n",
      "Done 120 out of 366\n",
      "Done 121 out of 366\n",
      "Done 122 out of 366\n",
      "Done 123 out of 366\n",
      "Done 124 out of 366\n",
      "Done 125 out of 366\n",
      "Done 126 out of 366\n",
      "Done 127 out of 366\n",
      "Done 128 out of 366\n",
      "Done 129 out of 366\n",
      "Done 130 out of 366\n",
      "Done 131 out of 366\n",
      "Done 132 out of 366\n",
      "Done 133 out of 366\n",
      "Done 134 out of 366\n",
      "Done 135 out of 366\n",
      "Done 136 out of 366\n",
      "Done 137 out of 366\n",
      "Done 138 out of 366\n",
      "Done 139 out of 366\n",
      "Done 140 out of 366\n",
      "Done 141 out of 366\n",
      "Done 142 out of 366\n",
      "Done 143 out of 366\n",
      "Done 144 out of 366\n",
      "Done 145 out of 366\n",
      "Done 146 out of 366\n",
      "Done 147 out of 366\n",
      "Done 148 out of 366\n",
      "Done 149 out of 366\n",
      "Done 150 out of 366\n",
      "Done 151 out of 366\n",
      "Done 152 out of 366\n",
      "Done 153 out of 366\n",
      "Done 154 out of 366\n",
      "Done 155 out of 366\n",
      "Done 156 out of 366\n",
      "Done 157 out of 366\n",
      "Done 158 out of 366\n",
      "Done 159 out of 366\n",
      "Done 160 out of 366\n",
      "Done 161 out of 366\n",
      "Done 162 out of 366\n",
      "Done 163 out of 366\n",
      "Done 164 out of 366\n",
      "Done 165 out of 366\n",
      "Done 166 out of 366\n",
      "Done 167 out of 366\n",
      "Done 168 out of 366\n",
      "Done 169 out of 366\n",
      "Done 170 out of 366\n",
      "Done 171 out of 366\n",
      "Done 172 out of 366\n",
      "Done 173 out of 366\n",
      "Done 174 out of 366\n",
      "Done 175 out of 366\n",
      "Done 176 out of 366\n",
      "Done 177 out of 366\n",
      "Done 178 out of 366\n",
      "Done 179 out of 366\n",
      "Done 180 out of 366\n",
      "Done 181 out of 366\n",
      "Done 182 out of 366\n",
      "Done 183 out of 366\n",
      "Done 184 out of 366\n",
      "Done 185 out of 366\n",
      "Done 186 out of 366\n",
      "Done 187 out of 366\n",
      "Done 188 out of 366\n",
      "Done 189 out of 366\n",
      "Done 190 out of 366\n",
      "Done 191 out of 366\n",
      "Done 192 out of 366\n",
      "Done 193 out of 366\n",
      "Done 194 out of 366\n",
      "Done 195 out of 366\n",
      "Done 196 out of 366\n",
      "Done 197 out of 366\n",
      "Done 198 out of 366\n",
      "Done 199 out of 366\n",
      "Done 200 out of 366\n",
      "Done 201 out of 366\n",
      "Done 202 out of 366\n",
      "Done 203 out of 366\n",
      "Done 204 out of 366\n",
      "Done 205 out of 366\n",
      "Done 206 out of 366\n",
      "Done 207 out of 366\n",
      "Done 208 out of 366\n",
      "Done 209 out of 366\n",
      "Done 210 out of 366\n",
      "Done 211 out of 366\n",
      "Done 212 out of 366\n",
      "Done 213 out of 366\n",
      "Done 214 out of 366\n",
      "Done 215 out of 366\n",
      "Done 216 out of 366\n",
      "Done 217 out of 366\n",
      "Done 218 out of 366\n",
      "Done 219 out of 366\n",
      "Done 220 out of 366\n",
      "Done 221 out of 366\n",
      "Done 222 out of 366\n",
      "Done 223 out of 366\n",
      "Done 224 out of 366\n",
      "Done 225 out of 366\n",
      "Done 226 out of 366\n",
      "Done 227 out of 366\n",
      "Done 228 out of 366\n",
      "Done 229 out of 366\n",
      "Done 230 out of 366\n",
      "Done 231 out of 366\n",
      "Done 232 out of 366\n",
      "Done 233 out of 366\n",
      "Done 234 out of 366\n",
      "Done 235 out of 366\n",
      "Done 236 out of 366\n",
      "Done 237 out of 366\n",
      "Done 238 out of 366\n",
      "Done 239 out of 366\n",
      "Done 240 out of 366\n",
      "Done 241 out of 366\n",
      "Done 242 out of 366\n",
      "Done 243 out of 366\n",
      "Done 244 out of 366\n",
      "Done 245 out of 366\n",
      "Done 246 out of 366\n",
      "Done 247 out of 366\n",
      "Done 248 out of 366\n",
      "Done 249 out of 366\n",
      "Done 250 out of 366\n",
      "Done 251 out of 366\n",
      "Done 252 out of 366\n",
      "Done 253 out of 366\n",
      "Done 254 out of 366\n",
      "Done 255 out of 366\n",
      "Done 256 out of 366\n",
      "Done 257 out of 366\n",
      "Done 258 out of 366\n",
      "Done 259 out of 366\n",
      "Done 260 out of 366\n",
      "Done 261 out of 366\n",
      "Done 262 out of 366\n",
      "Done 263 out of 366\n",
      "Done 264 out of 366\n",
      "Done 265 out of 366\n",
      "Done 266 out of 366\n",
      "Done 267 out of 366\n",
      "Done 268 out of 366\n",
      "Done 269 out of 366\n",
      "Done 270 out of 366\n",
      "Done 271 out of 366\n",
      "Done 272 out of 366\n",
      "Done 273 out of 366\n",
      "Done 274 out of 366\n",
      "Done 275 out of 366\n",
      "Done 276 out of 366\n",
      "Done 277 out of 366\n",
      "Done 278 out of 366\n",
      "Done 279 out of 366\n",
      "Done 280 out of 366\n",
      "Done 281 out of 366\n",
      "Done 282 out of 366\n",
      "Done 283 out of 366\n",
      "Done 284 out of 366\n",
      "Done 285 out of 366\n",
      "Done 286 out of 366\n",
      "Done 287 out of 366\n",
      "Done 288 out of 366\n",
      "Done 289 out of 366\n",
      "Done 290 out of 366\n",
      "Done 291 out of 366\n",
      "Done 292 out of 366\n",
      "Done 293 out of 366\n",
      "Done 294 out of 366\n",
      "Done 295 out of 366\n",
      "Done 296 out of 366\n",
      "Done 297 out of 366\n",
      "Done 298 out of 366\n",
      "Done 299 out of 366\n",
      "Done 300 out of 366\n",
      "Done 301 out of 366\n",
      "Done 302 out of 366\n",
      "Done 303 out of 366\n",
      "Done 304 out of 366\n",
      "Done 305 out of 366\n",
      "Done 306 out of 366\n",
      "Done 307 out of 366\n",
      "Done 308 out of 366\n",
      "Done 309 out of 366\n",
      "Done 310 out of 366\n",
      "Done 311 out of 366\n",
      "Done 312 out of 366\n",
      "Done 313 out of 366\n",
      "Done 314 out of 366\n",
      "Done 315 out of 366\n",
      "Done 316 out of 366\n",
      "Done 317 out of 366\n",
      "Done 318 out of 366\n",
      "Done 319 out of 366\n",
      "Done 320 out of 366\n",
      "Done 321 out of 366\n",
      "Done 322 out of 366\n",
      "Done 323 out of 366\n",
      "Done 324 out of 366\n",
      "Done 325 out of 366\n",
      "Done 326 out of 366\n",
      "Done 327 out of 366\n",
      "Done 328 out of 366\n",
      "Done 329 out of 366\n",
      "Done 330 out of 366\n",
      "Done 331 out of 366\n",
      "Done 332 out of 366\n",
      "Done 333 out of 366\n",
      "Done 334 out of 366\n",
      "Done 335 out of 366\n",
      "Done 336 out of 366\n",
      "Done 337 out of 366\n",
      "Done 338 out of 366\n",
      "Done 339 out of 366\n",
      "Done 340 out of 366\n",
      "Done 341 out of 366\n",
      "Done 342 out of 366\n",
      "Done 343 out of 366\n",
      "Done 344 out of 366\n",
      "Done 345 out of 366\n",
      "Done 346 out of 366\n",
      "Done 347 out of 366\n",
      "Done 348 out of 366\n",
      "Done 349 out of 366\n",
      "Done 350 out of 366\n",
      "Done 351 out of 366\n",
      "Done 352 out of 366\n",
      "Done 353 out of 366\n",
      "Done 354 out of 366\n",
      "Done 355 out of 366\n",
      "Done 356 out of 366\n",
      "Done 357 out of 366\n",
      "Done 358 out of 366\n",
      "Done 359 out of 366\n",
      "Done 360 out of 366\n",
      "Done 361 out of 366\n",
      "Done 362 out of 366\n",
      "Done 363 out of 366\n",
      "Done 364 out of 366\n",
      "Done 365 out of 366\n",
      "Working with pro no1 wnd ec00 mwh/h cet min15 f\n",
      "Done 1 out of 366\n",
      "Done 2 out of 366\n",
      "Done 3 out of 366\n",
      "Done 4 out of 366\n",
      "Done 5 out of 366\n",
      "Done 6 out of 366\n",
      "Done 7 out of 366\n",
      "Done 8 out of 366\n",
      "Done 9 out of 366\n",
      "Done 10 out of 366\n",
      "Done 11 out of 366\n",
      "Done 12 out of 366\n",
      "Done 13 out of 366\n",
      "Done 14 out of 366\n",
      "Done 15 out of 366\n",
      "Done 16 out of 366\n",
      "Done 17 out of 366\n",
      "Done 18 out of 366\n",
      "Done 19 out of 366\n",
      "Done 20 out of 366\n",
      "Done 21 out of 366\n",
      "Done 22 out of 366\n",
      "Done 23 out of 366\n",
      "Done 24 out of 366\n",
      "Done 25 out of 366\n",
      "Done 26 out of 366\n",
      "Done 27 out of 366\n",
      "Done 28 out of 366\n",
      "Done 29 out of 366\n",
      "Done 30 out of 366\n",
      "Done 31 out of 366\n",
      "Done 32 out of 366\n",
      "Done 33 out of 366\n",
      "Done 34 out of 366\n",
      "Done 35 out of 366\n",
      "Done 36 out of 366\n",
      "Done 37 out of 366\n",
      "Done 38 out of 366\n",
      "Done 39 out of 366\n",
      "Done 40 out of 366\n",
      "Done 41 out of 366\n",
      "Done 42 out of 366\n",
      "Done 43 out of 366\n",
      "Done 44 out of 366\n",
      "Done 45 out of 366\n",
      "Done 46 out of 366\n",
      "Done 47 out of 366\n",
      "Done 48 out of 366\n",
      "Done 49 out of 366\n",
      "Done 50 out of 366\n",
      "Done 51 out of 366\n",
      "Done 52 out of 366\n",
      "Done 53 out of 366\n",
      "Done 54 out of 366\n",
      "Done 55 out of 366\n",
      "Done 56 out of 366\n",
      "Done 57 out of 366\n",
      "Done 58 out of 366\n",
      "Done 59 out of 366\n",
      "Done 60 out of 366\n",
      "Done 61 out of 366\n",
      "Done 62 out of 366\n",
      "Done 63 out of 366\n",
      "Done 64 out of 366\n",
      "Done 65 out of 366\n",
      "Done 66 out of 366\n",
      "Done 67 out of 366\n",
      "Done 68 out of 366\n",
      "Done 69 out of 366\n",
      "Done 70 out of 366\n",
      "Done 71 out of 366\n",
      "Done 72 out of 366\n",
      "Done 73 out of 366\n",
      "Done 74 out of 366\n",
      "Done 75 out of 366\n",
      "Done 76 out of 366\n",
      "Done 77 out of 366\n",
      "Done 78 out of 366\n",
      "Done 79 out of 366\n",
      "Done 80 out of 366\n",
      "Done 81 out of 366\n",
      "Done 82 out of 366\n",
      "Done 83 out of 366\n",
      "Done 84 out of 366\n",
      "Done 85 out of 366\n",
      "Done 86 out of 366\n",
      "Done 87 out of 366\n",
      "Done 88 out of 366\n",
      "Done 89 out of 366\n",
      "Done 90 out of 366\n",
      "Done 91 out of 366\n",
      "Done 92 out of 366\n",
      "Done 93 out of 366\n",
      "Done 94 out of 366\n",
      "Done 95 out of 366\n",
      "Done 96 out of 366\n",
      "Done 97 out of 366\n",
      "Done 98 out of 366\n",
      "Done 99 out of 366\n",
      "Done 100 out of 366\n",
      "Done 101 out of 366\n",
      "Done 102 out of 366\n",
      "Done 103 out of 366\n",
      "Done 104 out of 366\n",
      "Done 105 out of 366\n",
      "Done 106 out of 366\n",
      "Done 107 out of 366\n",
      "Done 108 out of 366\n",
      "Done 109 out of 366\n",
      "Done 110 out of 366\n",
      "Done 111 out of 366\n",
      "Done 112 out of 366\n",
      "Done 113 out of 366\n",
      "Done 114 out of 366\n",
      "Done 115 out of 366\n",
      "Done 116 out of 366\n",
      "Done 117 out of 366\n",
      "Done 118 out of 366\n",
      "Done 119 out of 366\n",
      "Done 120 out of 366\n",
      "Done 121 out of 366\n",
      "Done 122 out of 366\n",
      "Done 123 out of 366\n",
      "Done 124 out of 366\n",
      "Done 125 out of 366\n",
      "Done 126 out of 366\n",
      "Done 127 out of 366\n",
      "Done 128 out of 366\n",
      "Done 129 out of 366\n",
      "Done 130 out of 366\n",
      "Done 131 out of 366\n",
      "Done 132 out of 366\n",
      "Done 133 out of 366\n",
      "Done 134 out of 366\n",
      "Done 135 out of 366\n",
      "Done 136 out of 366\n",
      "Done 137 out of 366\n",
      "Done 138 out of 366\n",
      "Done 139 out of 366\n",
      "Done 140 out of 366\n",
      "Done 141 out of 366\n",
      "Done 142 out of 366\n",
      "Done 143 out of 366\n",
      "Done 144 out of 366\n",
      "Done 145 out of 366\n",
      "Done 146 out of 366\n",
      "Done 147 out of 366\n",
      "Done 148 out of 366\n",
      "Done 149 out of 366\n",
      "Done 150 out of 366\n",
      "Done 151 out of 366\n",
      "Done 152 out of 366\n",
      "Done 153 out of 366\n",
      "Done 154 out of 366\n",
      "Done 155 out of 366\n",
      "Done 156 out of 366\n",
      "Done 157 out of 366\n",
      "Done 158 out of 366\n",
      "Done 159 out of 366\n",
      "Done 160 out of 366\n",
      "Done 161 out of 366\n",
      "Done 162 out of 366\n",
      "Done 163 out of 366\n",
      "Done 164 out of 366\n",
      "Done 165 out of 366\n",
      "Done 166 out of 366\n",
      "Done 167 out of 366\n",
      "Done 168 out of 366\n",
      "Done 169 out of 366\n",
      "Done 170 out of 366\n",
      "Done 171 out of 366\n",
      "Done 172 out of 366\n",
      "Done 173 out of 366\n",
      "Done 174 out of 366\n",
      "Done 175 out of 366\n",
      "Done 176 out of 366\n",
      "Done 177 out of 366\n",
      "Done 178 out of 366\n",
      "Done 179 out of 366\n",
      "Done 180 out of 366\n",
      "Done 181 out of 366\n",
      "Done 182 out of 366\n",
      "Done 183 out of 366\n",
      "Done 184 out of 366\n",
      "Done 185 out of 366\n",
      "Done 186 out of 366\n",
      "Done 187 out of 366\n",
      "Done 188 out of 366\n",
      "Done 189 out of 366\n",
      "Done 190 out of 366\n",
      "Done 191 out of 366\n",
      "Done 192 out of 366\n",
      "Done 193 out of 366\n",
      "Done 194 out of 366\n",
      "Done 195 out of 366\n",
      "Done 196 out of 366\n",
      "Done 197 out of 366\n",
      "Done 198 out of 366\n",
      "Done 199 out of 366\n",
      "Done 200 out of 366\n",
      "Done 201 out of 366\n",
      "Done 202 out of 366\n",
      "Done 203 out of 366\n",
      "Done 204 out of 366\n",
      "Done 205 out of 366\n",
      "Done 206 out of 366\n",
      "Done 207 out of 366\n",
      "Done 208 out of 366\n",
      "Done 209 out of 366\n",
      "Done 210 out of 366\n",
      "Done 211 out of 366\n",
      "Done 212 out of 366\n",
      "Done 213 out of 366\n",
      "Done 214 out of 366\n",
      "Done 215 out of 366\n",
      "Done 216 out of 366\n",
      "Done 217 out of 366\n",
      "Done 218 out of 366\n",
      "Done 219 out of 366\n",
      "Done 220 out of 366\n",
      "Done 221 out of 366\n",
      "Done 222 out of 366\n",
      "Done 223 out of 366\n",
      "Done 224 out of 366\n",
      "Done 225 out of 366\n",
      "Done 226 out of 366\n",
      "Done 227 out of 366\n",
      "Done 228 out of 366\n",
      "Done 229 out of 366\n",
      "Done 230 out of 366\n",
      "Done 231 out of 366\n",
      "Done 232 out of 366\n",
      "Done 233 out of 366\n",
      "Done 234 out of 366\n",
      "Done 235 out of 366\n",
      "Done 236 out of 366\n",
      "Done 237 out of 366\n",
      "Done 238 out of 366\n",
      "Done 239 out of 366\n",
      "Done 240 out of 366\n",
      "Done 241 out of 366\n",
      "Done 242 out of 366\n",
      "Done 243 out of 366\n",
      "Done 244 out of 366\n",
      "Done 245 out of 366\n",
      "Done 246 out of 366\n",
      "Done 247 out of 366\n",
      "Done 248 out of 366\n",
      "Done 249 out of 366\n",
      "Done 250 out of 366\n",
      "Done 251 out of 366\n",
      "Done 252 out of 366\n",
      "Done 253 out of 366\n",
      "Done 254 out of 366\n",
      "Done 255 out of 366\n",
      "Done 256 out of 366\n",
      "Done 257 out of 366\n",
      "Done 258 out of 366\n",
      "Done 259 out of 366\n",
      "Done 260 out of 366\n",
      "Done 261 out of 366\n",
      "Done 262 out of 366\n",
      "Done 263 out of 366\n",
      "Done 264 out of 366\n",
      "Done 265 out of 366\n",
      "Done 266 out of 366\n",
      "Done 267 out of 366\n",
      "Done 268 out of 366\n",
      "Done 269 out of 366\n",
      "Done 270 out of 366\n",
      "Done 271 out of 366\n",
      "Done 272 out of 366\n",
      "Done 273 out of 366\n",
      "Done 274 out of 366\n",
      "Done 275 out of 366\n",
      "Done 276 out of 366\n",
      "Done 277 out of 366\n",
      "Done 278 out of 366\n",
      "Done 279 out of 366\n",
      "Done 280 out of 366\n",
      "Done 281 out of 366\n",
      "Done 282 out of 366\n",
      "Done 283 out of 366\n",
      "Done 284 out of 366\n",
      "Done 285 out of 366\n",
      "Done 286 out of 366\n",
      "Done 287 out of 366\n",
      "Done 288 out of 366\n",
      "Done 289 out of 366\n",
      "Done 290 out of 366\n",
      "Done 291 out of 366\n",
      "Done 292 out of 366\n",
      "Done 293 out of 366\n",
      "Done 294 out of 366\n",
      "Done 295 out of 366\n",
      "Done 296 out of 366\n",
      "Done 297 out of 366\n",
      "Done 298 out of 366\n",
      "Done 299 out of 366\n",
      "Done 300 out of 366\n",
      "Done 301 out of 366\n",
      "Done 302 out of 366\n",
      "Done 303 out of 366\n",
      "Done 304 out of 366\n",
      "Done 305 out of 366\n",
      "Done 306 out of 366\n",
      "Done 307 out of 366\n",
      "Done 308 out of 366\n",
      "Done 309 out of 366\n",
      "Done 310 out of 366\n",
      "Done 311 out of 366\n",
      "Done 312 out of 366\n",
      "Done 313 out of 366\n",
      "Done 314 out of 366\n",
      "Done 315 out of 366\n",
      "Done 316 out of 366\n",
      "Done 317 out of 366\n",
      "Done 318 out of 366\n",
      "Done 319 out of 366\n",
      "Done 320 out of 366\n",
      "Done 321 out of 366\n",
      "Done 322 out of 366\n",
      "Done 323 out of 366\n",
      "Done 324 out of 366\n",
      "Done 325 out of 366\n",
      "Done 326 out of 366\n",
      "Done 327 out of 366\n",
      "Done 328 out of 366\n",
      "Done 329 out of 366\n",
      "Done 330 out of 366\n",
      "Done 331 out of 366\n",
      "Done 332 out of 366\n",
      "Done 333 out of 366\n",
      "Done 334 out of 366\n",
      "Done 335 out of 366\n",
      "Done 336 out of 366\n",
      "Done 337 out of 366\n",
      "Done 338 out of 366\n",
      "Done 339 out of 366\n",
      "Done 340 out of 366\n",
      "Done 341 out of 366\n",
      "Done 342 out of 366\n",
      "Done 343 out of 366\n",
      "Done 344 out of 366\n",
      "Done 345 out of 366\n",
      "Done 346 out of 366\n",
      "Done 347 out of 366\n",
      "Done 348 out of 366\n",
      "Done 349 out of 366\n",
      "Done 350 out of 366\n",
      "Done 351 out of 366\n",
      "Done 352 out of 366\n",
      "Done 353 out of 366\n",
      "Done 354 out of 366\n",
      "Done 355 out of 366\n",
      "Done 356 out of 366\n",
      "Done 357 out of 366\n",
      "Done 358 out of 366\n",
      "Done 359 out of 366\n",
      "Done 360 out of 366\n",
      "Done 361 out of 366\n",
      "Done 362 out of 366\n",
      "Done 363 out of 366\n",
      "Done 364 out of 366\n",
      "Done 365 out of 366\n",
      "Working with pro no1 spv ec00 mwh/h cet min15 f\n",
      "Done 1 out of 366\n",
      "Done 2 out of 366\n",
      "Done 3 out of 366\n",
      "Done 4 out of 366\n",
      "Done 5 out of 366\n",
      "Done 6 out of 366\n",
      "Done 7 out of 366\n",
      "Done 8 out of 366\n",
      "Done 9 out of 366\n",
      "Done 10 out of 366\n",
      "Done 11 out of 366\n",
      "Done 12 out of 366\n",
      "Done 13 out of 366\n",
      "Done 14 out of 366\n",
      "Done 15 out of 366\n",
      "Done 16 out of 366\n",
      "Done 17 out of 366\n",
      "Done 18 out of 366\n",
      "Done 19 out of 366\n",
      "Done 20 out of 366\n",
      "Done 21 out of 366\n",
      "Done 22 out of 366\n",
      "Done 23 out of 366\n",
      "Done 24 out of 366\n",
      "Done 25 out of 366\n",
      "Done 26 out of 366\n",
      "Done 27 out of 366\n",
      "Done 28 out of 366\n",
      "Done 29 out of 366\n",
      "Done 30 out of 366\n",
      "Done 31 out of 366\n",
      "Done 32 out of 366\n",
      "Done 33 out of 366\n",
      "Done 34 out of 366\n",
      "Done 35 out of 366\n",
      "Done 36 out of 366\n",
      "Done 37 out of 366\n",
      "Done 38 out of 366\n",
      "Done 39 out of 366\n",
      "Done 40 out of 366\n",
      "Done 41 out of 366\n",
      "Done 42 out of 366\n",
      "Done 43 out of 366\n",
      "Done 44 out of 366\n",
      "Done 45 out of 366\n",
      "Done 46 out of 366\n",
      "Done 47 out of 366\n",
      "Done 48 out of 366\n",
      "Done 49 out of 366\n",
      "Done 50 out of 366\n",
      "Done 51 out of 366\n",
      "Done 52 out of 366\n",
      "Done 53 out of 366\n",
      "Done 54 out of 366\n",
      "Done 55 out of 366\n",
      "Done 56 out of 366\n",
      "Done 57 out of 366\n",
      "Done 58 out of 366\n",
      "Done 59 out of 366\n",
      "Done 60 out of 366\n",
      "Done 61 out of 366\n",
      "Done 62 out of 366\n",
      "Done 63 out of 366\n",
      "Done 64 out of 366\n",
      "Done 65 out of 366\n",
      "Done 66 out of 366\n",
      "Done 67 out of 366\n",
      "Done 68 out of 366\n",
      "Done 69 out of 366\n",
      "Done 70 out of 366\n",
      "Done 71 out of 366\n",
      "Done 72 out of 366\n",
      "Done 73 out of 366\n",
      "Done 74 out of 366\n",
      "Done 75 out of 366\n",
      "Done 76 out of 366\n",
      "Done 77 out of 366\n",
      "Done 78 out of 366\n",
      "Done 79 out of 366\n",
      "Done 80 out of 366\n",
      "Done 81 out of 366\n",
      "Done 82 out of 366\n",
      "Done 83 out of 366\n",
      "Done 84 out of 366\n",
      "Done 85 out of 366\n",
      "Done 86 out of 366\n",
      "Done 87 out of 366\n",
      "Done 88 out of 366\n",
      "Done 89 out of 366\n",
      "Done 90 out of 366\n",
      "Done 91 out of 366\n",
      "Done 92 out of 366\n",
      "Done 93 out of 366\n",
      "Done 94 out of 366\n",
      "Done 95 out of 366\n",
      "Done 96 out of 366\n",
      "Done 97 out of 366\n",
      "Done 98 out of 366\n",
      "Done 99 out of 366\n",
      "Done 100 out of 366\n",
      "Done 101 out of 366\n",
      "Done 102 out of 366\n",
      "Done 103 out of 366\n",
      "Done 104 out of 366\n",
      "Done 105 out of 366\n",
      "Done 106 out of 366\n",
      "Done 107 out of 366\n",
      "Done 108 out of 366\n",
      "Done 109 out of 366\n",
      "Done 110 out of 366\n",
      "Done 111 out of 366\n",
      "Done 112 out of 366\n",
      "Done 113 out of 366\n",
      "Done 114 out of 366\n",
      "Done 115 out of 366\n",
      "Done 116 out of 366\n",
      "Done 117 out of 366\n",
      "Done 118 out of 366\n",
      "Done 119 out of 366\n",
      "Done 120 out of 366\n",
      "Done 121 out of 366\n",
      "Done 122 out of 366\n",
      "Done 123 out of 366\n",
      "Done 124 out of 366\n",
      "Done 125 out of 366\n",
      "Done 126 out of 366\n",
      "Done 127 out of 366\n",
      "Done 128 out of 366\n",
      "Done 129 out of 366\n",
      "Done 130 out of 366\n",
      "Done 131 out of 366\n",
      "Done 132 out of 366\n",
      "Done 133 out of 366\n",
      "Done 134 out of 366\n",
      "Done 135 out of 366\n",
      "Done 136 out of 366\n",
      "Done 137 out of 366\n",
      "Done 138 out of 366\n",
      "Done 139 out of 366\n",
      "Done 140 out of 366\n",
      "Done 141 out of 366\n",
      "Done 142 out of 366\n",
      "Done 143 out of 366\n",
      "Done 144 out of 366\n",
      "Done 145 out of 366\n",
      "Done 146 out of 366\n",
      "Done 147 out of 366\n",
      "Done 148 out of 366\n",
      "Done 149 out of 366\n",
      "Done 150 out of 366\n",
      "Done 151 out of 366\n",
      "Done 152 out of 366\n",
      "Done 153 out of 366\n",
      "Done 154 out of 366\n",
      "Done 155 out of 366\n",
      "Done 156 out of 366\n",
      "Done 157 out of 366\n",
      "Done 158 out of 366\n",
      "Done 159 out of 366\n",
      "Done 160 out of 366\n",
      "Done 161 out of 366\n",
      "Done 162 out of 366\n",
      "Done 163 out of 366\n",
      "Done 164 out of 366\n",
      "Done 165 out of 366\n",
      "Done 166 out of 366\n",
      "Done 167 out of 366\n",
      "Done 168 out of 366\n",
      "Done 169 out of 366\n",
      "Done 170 out of 366\n",
      "Done 171 out of 366\n",
      "Done 172 out of 366\n",
      "Done 173 out of 366\n",
      "Done 174 out of 366\n",
      "Done 175 out of 366\n",
      "Done 176 out of 366\n",
      "Done 177 out of 366\n",
      "Done 178 out of 366\n",
      "Done 179 out of 366\n",
      "Done 180 out of 366\n",
      "Done 181 out of 366\n",
      "Done 182 out of 366\n",
      "Done 183 out of 366\n",
      "Done 184 out of 366\n",
      "Done 185 out of 366\n",
      "Done 186 out of 366\n",
      "Done 187 out of 366\n",
      "Done 188 out of 366\n",
      "Done 189 out of 366\n",
      "Done 190 out of 366\n",
      "Done 191 out of 366\n",
      "Done 192 out of 366\n",
      "Done 193 out of 366\n",
      "Done 194 out of 366\n",
      "Done 195 out of 366\n",
      "Done 196 out of 366\n",
      "Done 197 out of 366\n",
      "Done 198 out of 366\n",
      "Done 199 out of 366\n",
      "Done 200 out of 366\n",
      "Done 201 out of 366\n",
      "Done 202 out of 366\n",
      "Done 203 out of 366\n",
      "Done 204 out of 366\n",
      "Done 205 out of 366\n",
      "Done 206 out of 366\n",
      "Done 207 out of 366\n",
      "Done 208 out of 366\n",
      "Done 209 out of 366\n",
      "Done 210 out of 366\n",
      "Done 211 out of 366\n",
      "Done 212 out of 366\n",
      "Done 213 out of 366\n",
      "Done 214 out of 366\n",
      "Done 215 out of 366\n",
      "Done 216 out of 366\n",
      "Done 217 out of 366\n",
      "Done 218 out of 366\n",
      "Done 219 out of 366\n",
      "Done 220 out of 366\n",
      "Done 221 out of 366\n",
      "Done 222 out of 366\n",
      "Done 223 out of 366\n",
      "Done 224 out of 366\n",
      "Done 225 out of 366\n",
      "Done 226 out of 366\n",
      "Done 227 out of 366\n",
      "Done 228 out of 366\n",
      "Done 229 out of 366\n",
      "Done 230 out of 366\n",
      "Done 231 out of 366\n",
      "Done 232 out of 366\n",
      "Done 233 out of 366\n",
      "Done 234 out of 366\n",
      "Done 235 out of 366\n",
      "Done 236 out of 366\n",
      "Done 237 out of 366\n",
      "Done 238 out of 366\n",
      "Done 239 out of 366\n",
      "Done 240 out of 366\n",
      "Done 241 out of 366\n",
      "Done 242 out of 366\n",
      "Done 243 out of 366\n",
      "Done 244 out of 366\n",
      "Done 245 out of 366\n",
      "Done 246 out of 366\n",
      "Done 247 out of 366\n",
      "Done 248 out of 366\n",
      "Done 249 out of 366\n",
      "Done 250 out of 366\n",
      "Done 251 out of 366\n",
      "Done 252 out of 366\n",
      "Done 253 out of 366\n",
      "Done 254 out of 366\n",
      "Done 255 out of 366\n",
      "Done 256 out of 366\n",
      "Done 257 out of 366\n",
      "Done 258 out of 366\n",
      "Done 259 out of 366\n",
      "Done 260 out of 366\n",
      "Done 261 out of 366\n",
      "Done 262 out of 366\n",
      "Done 263 out of 366\n",
      "Done 264 out of 366\n",
      "Done 265 out of 366\n",
      "Done 266 out of 366\n",
      "Done 267 out of 366\n",
      "Done 268 out of 366\n",
      "Done 269 out of 366\n",
      "Done 270 out of 366\n",
      "Done 271 out of 366\n",
      "Done 272 out of 366\n",
      "Done 273 out of 366\n",
      "Done 274 out of 366\n",
      "Done 275 out of 366\n",
      "Done 276 out of 366\n",
      "Done 277 out of 366\n",
      "Done 278 out of 366\n",
      "Done 279 out of 366\n",
      "Done 280 out of 366\n",
      "Done 281 out of 366\n",
      "Done 282 out of 366\n",
      "Done 283 out of 366\n",
      "Done 284 out of 366\n",
      "Done 285 out of 366\n",
      "Done 286 out of 366\n",
      "Done 287 out of 366\n",
      "Done 288 out of 366\n",
      "Done 289 out of 366\n",
      "Done 290 out of 366\n",
      "Done 291 out of 366\n",
      "Done 292 out of 366\n",
      "Done 293 out of 366\n",
      "Done 294 out of 366\n",
      "Done 295 out of 366\n",
      "Done 296 out of 366\n",
      "Done 297 out of 366\n",
      "Done 298 out of 366\n",
      "Done 299 out of 366\n",
      "Done 300 out of 366\n",
      "Done 301 out of 366\n",
      "Done 302 out of 366\n",
      "Done 303 out of 366\n",
      "Done 304 out of 366\n",
      "Done 305 out of 366\n",
      "Done 306 out of 366\n",
      "Done 307 out of 366\n",
      "Done 308 out of 366\n",
      "Done 309 out of 366\n",
      "Done 310 out of 366\n",
      "Done 311 out of 366\n",
      "Done 312 out of 366\n",
      "Done 313 out of 366\n",
      "Done 314 out of 366\n",
      "Done 315 out of 366\n",
      "Done 316 out of 366\n",
      "Done 317 out of 366\n",
      "Done 318 out of 366\n",
      "Done 319 out of 366\n",
      "Done 320 out of 366\n",
      "Done 321 out of 366\n",
      "Done 322 out of 366\n",
      "Done 323 out of 366\n",
      "Done 324 out of 366\n",
      "Done 325 out of 366\n",
      "Done 326 out of 366\n",
      "Done 327 out of 366\n",
      "Done 328 out of 366\n",
      "Done 329 out of 366\n",
      "Done 330 out of 366\n",
      "Done 331 out of 366\n",
      "Done 332 out of 366\n",
      "Done 333 out of 366\n",
      "Done 334 out of 366\n",
      "Done 335 out of 366\n",
      "Done 336 out of 366\n",
      "Done 337 out of 366\n",
      "Done 338 out of 366\n",
      "Done 339 out of 366\n",
      "Done 340 out of 366\n",
      "Done 341 out of 366\n",
      "Done 342 out of 366\n",
      "Done 343 out of 366\n",
      "Done 344 out of 366\n",
      "Done 345 out of 366\n",
      "Done 346 out of 366\n",
      "Done 347 out of 366\n",
      "Done 348 out of 366\n",
      "Done 349 out of 366\n",
      "Done 350 out of 366\n",
      "Done 351 out of 366\n",
      "Done 352 out of 366\n",
      "Done 353 out of 366\n",
      "Done 354 out of 366\n",
      "Done 355 out of 366\n",
      "Done 356 out of 366\n",
      "Done 357 out of 366\n",
      "Done 358 out of 366\n",
      "Done 359 out of 366\n",
      "Done 360 out of 366\n",
      "Done 361 out of 366\n",
      "Done 362 out of 366\n",
      "Done 363 out of 366\n",
      "Done 364 out of 366\n",
      "Done 365 out of 366\n"
     ]
    }
   ],
   "source": [
    "import wapi # wattsights egen pakke / den må installers via `pip install wapi-python`\n",
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "from datetime import datetime \n",
    "from datetime import timedelta\n",
    "\n",
    "# Create a session\n",
    "# Dette forutsetter at dere har lagret volue credentials som miljøvariabler på PC'en deres og med navnet `WS_CLIENT_ID` og `WS_CLIENT_SECRET`\n",
    "user = os.getenv(\"WS_CLIENT_ID\")\n",
    "password = os.getenv(\"WS_CLIENT_SECRET\")\n",
    "session = wapi.Session(client_id=user, client_secret=password)\n",
    "\n",
    "start = '2021-06-16'\n",
    "end = '2022-06-16'\n",
    "periods=pd.date_range(start,end,freq='D')\n",
    "\n",
    "curves = ['con no1 mwh/h cet min15 a',\n",
    "'tt no1 con ec00 °c cet min15 f',\n",
    "'pro no1 wnd ec00 mwh/h cet min15 f',\n",
    "'pro no1 spv ec00 mwh/h cet min15 f'\n",
    "]\n",
    "\n",
    "df_watt = pd.DataFrame()\n",
    "for curve in curves:\n",
    "    print(f\"Working with {curve}\")\n",
    "    curve = session.get_curve(name=curve)\n",
    "    curve_type = curve.curve_type\n",
    "    res = []\n",
    "    counter = 1\n",
    "    if curve_type == 'TIME_SERIES':\n",
    "        ts = curve.get_data(data_from=start, data_to=end, function='SAVERAGE', frequency='MIN15')\n",
    "        watt_temp = ts.to_pandas()\n",
    "        df_watt = pd.merge(df_watt, watt_temp, left_index=True, right_index=True, how='outer')\n",
    "        continue\n",
    "    \n",
    "    for j in range(len(periods)):\n",
    "        if j == len(periods)-1:\n",
    "            continue\n",
    "        if curve_type == 'TAGGED_INSTANCES':\n",
    "            ts_list = curve.search_instances(issue_date_from=periods[j],\n",
    "                                 issue_date_to=periods[j+1],\n",
    "                                 with_data=True, function='SAVERAGE', frequency='MIN15', tags=['Avg'])\n",
    "        else: #curve_type == 'INSTANCES'\n",
    "            ts_list = curve.search_instances(issue_date_from=periods[j],\n",
    "                                 issue_date_to=periods[j+1],\n",
    "                                 with_data=True, function='SAVERAGE', frequency='MIN15')\n",
    "        # check if ts_list is empty and if yes, jump over that timestamp\n",
    "        if not ts_list:\n",
    "            continue\n",
    "        pd_s = ts_list[0].to_pandas()\n",
    "        if pd_s.empty:\n",
    "            continue\n",
    "        pd_s = pd_s.sort_index(ascending=True)\n",
    "        pd_s = pd_s[:24]\n",
    "        res.append(pd_s)\n",
    "        print(f'Done {counter} out of {len(periods)}')\n",
    "        counter += 1\n",
    "\n",
    "    for k in range(len(res)):\n",
    "        if k == 0:\n",
    "            watt = pd.Series(res[k])\n",
    "        else:\n",
    "            watt = pd.concat([watt, res[k]])\n",
    "    watt_temp = watt.rename(curve)\n",
    "    df_watt = pd.merge(df_watt, watt_temp, left_index=True, right_index=True, how='outer')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cons_actual</th>\n",
       "      <th>temp_forecast</th>\n",
       "      <th>wind_forecast</th>\n",
       "      <th>solar_forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-06-15 04:45:00+02:00</th>\n",
       "      <td>2128.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>51.477353</td>\n",
       "      <td>0.004659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-15 05:00:00+02:00</th>\n",
       "      <td>2195.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>51.477353</td>\n",
       "      <td>0.033182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-15 05:15:00+02:00</th>\n",
       "      <td>2195.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>51.477353</td>\n",
       "      <td>0.033182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-15 05:30:00+02:00</th>\n",
       "      <td>2195.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>51.477353</td>\n",
       "      <td>0.033182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-15 05:45:00+02:00</th>\n",
       "      <td>2195.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>51.477353</td>\n",
       "      <td>0.033182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           cons_actual  temp_forecast  wind_forecast  \\\n",
       "2022-06-15 04:45:00+02:00       2128.0           11.0      51.477353   \n",
       "2022-06-15 05:00:00+02:00       2195.0           11.4      51.477353   \n",
       "2022-06-15 05:15:00+02:00       2195.0           11.4      51.477353   \n",
       "2022-06-15 05:30:00+02:00       2195.0           11.4      51.477353   \n",
       "2022-06-15 05:45:00+02:00       2195.0           11.4      51.477353   \n",
       "\n",
       "                           solar_forecast  \n",
       "2022-06-15 04:45:00+02:00        0.004659  \n",
       "2022-06-15 05:00:00+02:00        0.033182  \n",
       "2022-06-15 05:15:00+02:00        0.033182  \n",
       "2022-06-15 05:30:00+02:00        0.033182  \n",
       "2022-06-15 05:45:00+02:00        0.033182  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_watt.columns = [\"cons_actual\", \"temp_forecast\", \"wind_forecast\", \"solar_forecast\"]\n",
    "df_watt.dropna(inplace=True)\n",
    "df_watt.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding Day- and Week- cos and sin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cons_actual</th>\n",
       "      <th>temp_forecast</th>\n",
       "      <th>wind_forecast</th>\n",
       "      <th>solar_forecast</th>\n",
       "      <th>Day sin</th>\n",
       "      <th>Day cos</th>\n",
       "      <th>Week sin</th>\n",
       "      <th>Week cos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-06-16 00:00:00+02:00</th>\n",
       "      <td>2591.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>48.654523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>-0.826239</td>\n",
       "      <td>0.563320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-16 00:15:00+02:00</th>\n",
       "      <td>2591.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>48.654523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.442289</td>\n",
       "      <td>0.896873</td>\n",
       "      <td>-0.820936</td>\n",
       "      <td>0.571021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-16 00:30:00+02:00</th>\n",
       "      <td>2591.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>48.654523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.382683</td>\n",
       "      <td>0.923880</td>\n",
       "      <td>-0.815561</td>\n",
       "      <td>0.578671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-16 00:45:00+02:00</th>\n",
       "      <td>2591.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>48.654523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.321439</td>\n",
       "      <td>0.946930</td>\n",
       "      <td>-0.810115</td>\n",
       "      <td>0.586271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-16 01:00:00+02:00</th>\n",
       "      <td>2473.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>45.029284</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>-0.804598</td>\n",
       "      <td>0.593820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           cons_actual  temp_forecast  wind_forecast  \\\n",
       "2021-06-16 00:00:00+02:00       2591.0           10.4      48.654523   \n",
       "2021-06-16 00:15:00+02:00       2591.0           10.4      48.654523   \n",
       "2021-06-16 00:30:00+02:00       2591.0           10.4      48.654523   \n",
       "2021-06-16 00:45:00+02:00       2591.0           10.4      48.654523   \n",
       "2021-06-16 01:00:00+02:00       2473.0            9.5      45.029284   \n",
       "\n",
       "                           solar_forecast   Day sin   Day cos  Week sin  \\\n",
       "2021-06-16 00:00:00+02:00             0.0 -0.500000  0.866025 -0.826239   \n",
       "2021-06-16 00:15:00+02:00             0.0 -0.442289  0.896873 -0.820936   \n",
       "2021-06-16 00:30:00+02:00             0.0 -0.382683  0.923880 -0.815561   \n",
       "2021-06-16 00:45:00+02:00             0.0 -0.321439  0.946930 -0.810115   \n",
       "2021-06-16 01:00:00+02:00             0.0 -0.258819  0.965926 -0.804598   \n",
       "\n",
       "                           Week cos  \n",
       "2021-06-16 00:00:00+02:00  0.563320  \n",
       "2021-06-16 00:15:00+02:00  0.571021  \n",
       "2021-06-16 00:30:00+02:00  0.578671  \n",
       "2021-06-16 00:45:00+02:00  0.586271  \n",
       "2021-06-16 01:00:00+02:00  0.593820  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_watt['Seconds'] = df_watt.index.map(pd.Timestamp.timestamp)\n",
    "day = 60*60*24\n",
    "year = 365.2425*day\n",
    "week = day*7\n",
    "df_watt['Day sin'] = np.sin(df_watt['Seconds'] * (2* np.pi / day))\n",
    "df_watt['Day cos'] = np.cos(df_watt['Seconds'] * (2 * np.pi / day))\n",
    "df_watt['Week sin'] = np.sin(df_watt['Seconds'] * (2 * np.pi / week))\n",
    "df_watt['Week cos'] = np.cos(df_watt['Seconds'] * (2 * np.pi / week))\n",
    "\n",
    "df_watt=df_watt.drop(['Seconds'], axis=1)\n",
    "df_watt.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cons_actual</th>\n",
       "      <th>temp_forecast</th>\n",
       "      <th>wind_forecast</th>\n",
       "      <th>solar_forecast</th>\n",
       "      <th>Day sin</th>\n",
       "      <th>Day cos</th>\n",
       "      <th>Week sin</th>\n",
       "      <th>Week cos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-06-16 00:00:00+02:00</th>\n",
       "      <td>2591.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>48.654523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>-0.826239</td>\n",
       "      <td>0.563320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-16 00:15:00+02:00</th>\n",
       "      <td>2591.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>48.654523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.442289</td>\n",
       "      <td>0.896873</td>\n",
       "      <td>-0.820936</td>\n",
       "      <td>0.571021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-16 00:30:00+02:00</th>\n",
       "      <td>2591.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>48.654523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.382683</td>\n",
       "      <td>0.923880</td>\n",
       "      <td>-0.815561</td>\n",
       "      <td>0.578671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-16 00:45:00+02:00</th>\n",
       "      <td>2591.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>48.654523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.321439</td>\n",
       "      <td>0.946930</td>\n",
       "      <td>-0.810115</td>\n",
       "      <td>0.586271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-16 01:00:00+02:00</th>\n",
       "      <td>2473.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>45.029284</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>-0.804598</td>\n",
       "      <td>0.593820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           cons_actual  temp_forecast  wind_forecast  \\\n",
       "2021-06-16 00:00:00+02:00       2591.0           10.4      48.654523   \n",
       "2021-06-16 00:15:00+02:00       2591.0           10.4      48.654523   \n",
       "2021-06-16 00:30:00+02:00       2591.0           10.4      48.654523   \n",
       "2021-06-16 00:45:00+02:00       2591.0           10.4      48.654523   \n",
       "2021-06-16 01:00:00+02:00       2473.0            9.5      45.029284   \n",
       "\n",
       "                           solar_forecast   Day sin   Day cos  Week sin  \\\n",
       "2021-06-16 00:00:00+02:00             0.0 -0.500000  0.866025 -0.826239   \n",
       "2021-06-16 00:15:00+02:00             0.0 -0.442289  0.896873 -0.820936   \n",
       "2021-06-16 00:30:00+02:00             0.0 -0.382683  0.923880 -0.815561   \n",
       "2021-06-16 00:45:00+02:00             0.0 -0.321439  0.946930 -0.810115   \n",
       "2021-06-16 01:00:00+02:00             0.0 -0.258819  0.965926 -0.804598   \n",
       "\n",
       "                           Week cos  \n",
       "2021-06-16 00:00:00+02:00  0.563320  \n",
       "2021-06-16 00:15:00+02:00  0.571021  \n",
       "2021-06-16 00:30:00+02:00  0.578671  \n",
       "2021-06-16 00:45:00+02:00  0.586271  \n",
       "2021-06-16 01:00:00+02:00  0.593820  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = list(df_watt)[0:8]\n",
    "#New dataframe with only training data - 4 columns\n",
    "df_for_training = df_watt[cols].astype(float)\n",
    "df_for_training.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.74205361,  0.83034725, -0.56282672, ...,  0.11990251,\n",
       "        -1.16604072,  0.79490613],\n",
       "       [-0.74205361,  0.83034725, -0.56282672, ...,  0.29901844,\n",
       "        -1.15853945,  0.80579413],\n",
       "       [-0.74205361,  0.83034725, -0.56282672, ...,  0.45583414,\n",
       "        -1.15093667,  0.81661156],\n",
       "       ...,\n",
       "       [-1.13141026,  0.97091746, -0.51383787, ..., -1.08018834,\n",
       "        -0.9881313 ,  1.00751328],\n",
       "       [-1.13141026,  0.97091746, -0.51383787, ..., -1.37390801,\n",
       "        -0.97864913,  1.01672927],\n",
       "       [-1.13141026,  0.97091746, -0.51383787, ..., -1.68276419,\n",
       "        -0.96908118,  1.02585623]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler = scaler.fit(df_for_training)\n",
    "df_for_training_scaled = scaler.transform(df_for_training)\n",
    "\n",
    "df_for_training_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reshape the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainX shape == (8742, 10, 8).\n",
      "trainY shape == (8742, 1).\n"
     ]
    }
   ],
   "source": [
    "#Empty lists to be populated using formatted training data\n",
    "trainX = []\n",
    "trainY = []\n",
    "\n",
    "n_future = 1   # Number of timesteps we want to look into the future based on the past timesteps.\n",
    "n_past = 10  # Number of past timesteps we want to use to predict the future.\n",
    "\n",
    "#Reformat input data into a shape: (n_samples x timesteps x n_features)\n",
    "#In my example, my df_for_training_scaled has a shape (12823, 5)\n",
    "#12823 refers to the number of data points and 5 refers to the columns (multi-variables).\n",
    "for i in range(n_past, len(df_for_training_scaled) - n_future +1):\n",
    "    trainX.append(df_for_training_scaled[i - n_past:i, 0:df_for_training.shape[1]])\n",
    "    trainY.append(df_for_training_scaled[i + n_future - 1:i + n_future, 0])\n",
    "\n",
    "trainX, trainY = np.array(trainX), np.array(trainY)\n",
    "\n",
    "print('trainX shape == {}.'.format(trainX.shape))\n",
    "print('trainY shape == {}.'.format(trainY.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_10\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_12 (LSTM)              (None, 10, 32)            5248      \n",
      "                                                                 \n",
      " lstm_13 (LSTM)              (None, 64)                24832     \n",
      "                                                                 \n",
      " dropout_7 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 30,145\n",
      "Trainable params: 30,145\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(32, activation='relu', input_shape=(trainX.shape[1], trainX.shape[2]), return_sequences=True))\n",
    "model.add(LSTM(64, activation='relu', return_sequences=False))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(trainY.shape[1]))\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "433/433 [==============================] - 6s 9ms/step - loss: 0.0623 - val_loss: 0.0250\n",
      "Epoch 2/10\n",
      "433/433 [==============================] - 4s 9ms/step - loss: 0.0231 - val_loss: 0.0161\n",
      "Epoch 3/10\n",
      "433/433 [==============================] - 4s 9ms/step - loss: 0.0208 - val_loss: 0.0156\n",
      "Epoch 4/10\n",
      "433/433 [==============================] - 4s 8ms/step - loss: 0.0196 - val_loss: 0.0114\n",
      "Epoch 5/10\n",
      "433/433 [==============================] - 3s 8ms/step - loss: 0.0189 - val_loss: 0.0113\n",
      "Epoch 6/10\n",
      "433/433 [==============================] - 4s 8ms/step - loss: 0.0174 - val_loss: 0.0085\n",
      "Epoch 7/10\n",
      "433/433 [==============================] - 3s 8ms/step - loss: 0.0172 - val_loss: 0.0111\n",
      "Epoch 8/10\n",
      "433/433 [==============================] - 3s 8ms/step - loss: 0.0168 - val_loss: 0.0090\n",
      "Epoch 9/10\n",
      "433/433 [==============================] - 4s 9ms/step - loss: 0.0157 - val_loss: 0.0117\n",
      "Epoch 10/10\n",
      "433/433 [==============================] - 3s 8ms/step - loss: 0.0162 - val_loss: 0.0060\n"
     ]
    }
   ],
   "source": [
    "n_past = 4 * 24 * 10 \n",
    "n_days_for_prediction= 24 * 4  #let us predict (<>*15min) hours\n",
    "\n",
    "history = model.fit(trainX[:-n_days_for_prediction], trainY[:-n_days_for_prediction], epochs=10, batch_size=16, validation_split=0.2, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24630575d60>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('dark_background')\n",
    "plt.plot(history.history['loss'], label='Training loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "#predict_period_dates = pd.date_range(list(train_dates)[-n_past], periods=n_days_for_prediction, freq=us_bd).tolist()\n",
    "#print(predict_period_dates)\n",
    "\n",
    "#Make prediction\n",
    "prediction = model.predict(trainX[-n_days_for_prediction:]) #shape = (n, 1) where n is the n_days_for_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96, 1)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Perform inverse transformation to rescale back to original range\n",
    "#Since we used 5 variables for transform, the inverse expects same dimensions\n",
    "#Therefore, let us copy our values 5 times and discard them after inverse transform\n",
    "prediction_copies = np.repeat(prediction, df_for_training.shape[1], axis=-1)\n",
    "y_pred_future = scaler.inverse_transform(prediction_copies)[:,0]\n",
    "y_pred_future.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.plot(df_watt.index[-n_days_for_prediction:], y_pred_future, label='prediction')\n",
    "plt.plot(df_watt['cons_actual'][-n_days_for_prediction:], label='cons_actual')\n",
    "#Beautify x-axis\n",
    "plt.xticks(rotation=90)\n",
    "plt.gcf().autofmt_xdate()\n",
    "myFmt = mdates.DateFormatter(\"%c\")\n",
    "plt.gca().xaxis.set_major_formatter(myFmt)\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This looks like a good fit, but lets add some quantifiers to how good it is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35040, 9)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_watt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.008413919247686863"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = model.evaluate(trainX, trainY, verbose=0)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I want to try the model on completely new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working with con no1 mwh/h cet min15 a\n",
      "Working with tt no1 con ec00 °c cet min15 f\n",
      "Done 1 out of 366\n",
      "Done 2 out of 366\n",
      "Done 3 out of 366\n",
      "Done 4 out of 366\n",
      "Done 5 out of 366\n",
      "Done 6 out of 366\n",
      "Done 7 out of 366\n",
      "Done 8 out of 366\n",
      "Done 9 out of 366\n",
      "Done 10 out of 366\n",
      "Done 11 out of 366\n",
      "Done 12 out of 366\n",
      "Done 13 out of 366\n",
      "Done 14 out of 366\n",
      "Done 15 out of 366\n",
      "Done 16 out of 366\n",
      "Done 17 out of 366\n",
      "Done 18 out of 366\n",
      "Done 19 out of 366\n",
      "Done 20 out of 366\n",
      "Done 21 out of 366\n",
      "Done 22 out of 366\n",
      "Done 23 out of 366\n",
      "Done 24 out of 366\n",
      "Done 25 out of 366\n",
      "Done 26 out of 366\n",
      "Done 27 out of 366\n",
      "Done 28 out of 366\n",
      "Done 29 out of 366\n",
      "Done 30 out of 366\n",
      "Done 31 out of 366\n",
      "Done 32 out of 366\n",
      "Done 33 out of 366\n",
      "Done 34 out of 366\n",
      "Done 35 out of 366\n",
      "Done 36 out of 366\n",
      "Done 37 out of 366\n",
      "Done 38 out of 366\n",
      "Done 39 out of 366\n",
      "Done 40 out of 366\n",
      "Done 41 out of 366\n",
      "Done 42 out of 366\n",
      "Done 43 out of 366\n",
      "Done 44 out of 366\n",
      "Done 45 out of 366\n",
      "Done 46 out of 366\n",
      "Done 47 out of 366\n",
      "Done 48 out of 366\n",
      "Done 49 out of 366\n",
      "Done 50 out of 366\n",
      "Done 51 out of 366\n",
      "Done 52 out of 366\n",
      "Done 53 out of 366\n",
      "Done 54 out of 366\n",
      "Done 55 out of 366\n",
      "Done 56 out of 366\n",
      "Done 57 out of 366\n",
      "Done 58 out of 366\n",
      "Done 59 out of 366\n",
      "Done 60 out of 366\n",
      "Done 61 out of 366\n",
      "Done 62 out of 366\n",
      "Done 63 out of 366\n",
      "Done 64 out of 366\n",
      "Done 65 out of 366\n",
      "Done 66 out of 366\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\57859\\Prosjekt\\TestLSTM.ipynb Cell 23'\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/57859/Prosjekt/TestLSTM.ipynb#ch0000022?line=28'>29</a>\u001b[0m     ts_list \u001b[39m=\u001b[39m curve\u001b[39m.\u001b[39msearch_instances(issue_date_from\u001b[39m=\u001b[39mperiods[j],\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/57859/Prosjekt/TestLSTM.ipynb#ch0000022?line=29'>30</a>\u001b[0m                          issue_date_to\u001b[39m=\u001b[39mperiods[j\u001b[39m+\u001b[39m\u001b[39m1\u001b[39m],\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/57859/Prosjekt/TestLSTM.ipynb#ch0000022?line=30'>31</a>\u001b[0m                          with_data\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, function\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mSAVERAGE\u001b[39m\u001b[39m'\u001b[39m, frequency\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mMIN15\u001b[39m\u001b[39m'\u001b[39m, tags\u001b[39m=\u001b[39m[\u001b[39m'\u001b[39m\u001b[39mAvg\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/57859/Prosjekt/TestLSTM.ipynb#ch0000022?line=31'>32</a>\u001b[0m \u001b[39melse\u001b[39;00m: \u001b[39m#curve_type == 'INSTANCES'\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/57859/Prosjekt/TestLSTM.ipynb#ch0000022?line=32'>33</a>\u001b[0m     ts_list \u001b[39m=\u001b[39m curve\u001b[39m.\u001b[39;49msearch_instances(issue_date_from\u001b[39m=\u001b[39;49mperiods[j],\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/57859/Prosjekt/TestLSTM.ipynb#ch0000022?line=33'>34</a>\u001b[0m                          issue_date_to\u001b[39m=\u001b[39;49mperiods[j\u001b[39m+\u001b[39;49m\u001b[39m1\u001b[39;49m],\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/57859/Prosjekt/TestLSTM.ipynb#ch0000022?line=34'>35</a>\u001b[0m                          with_data\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, function\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mSAVERAGE\u001b[39;49m\u001b[39m'\u001b[39;49m, frequency\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mMIN15\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/57859/Prosjekt/TestLSTM.ipynb#ch0000022?line=35'>36</a>\u001b[0m \u001b[39m# check if ts_list is empty and if yes, jump over that timestamp\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/57859/Prosjekt/TestLSTM.ipynb#ch0000022?line=36'>37</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m ts_list:\n",
      "File \u001b[1;32mc:\\Users\\57859\\.conda\\envs\\MachineLearning\\lib\\site-packages\\wapi\\curves.py:399\u001b[0m, in \u001b[0;36mInstanceCurve.search_instances\u001b[1;34m(self, issue_date_from, issue_date_to, issue_dates, issue_weekdays, issue_days, issue_months, issue_times, with_data, data_from, data_to, time_zone, filter, function, frequency, output_time_zone, only_accessible, modified_since)\u001b[0m\n\u001b[0;32m    397\u001b[0m astr \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m&\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mjoin(args)\n\u001b[0;32m    398\u001b[0m url \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m/api/instances/\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m?\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mid, astr)\n\u001b[1;32m--> 399\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_load_data(url, \u001b[39m'\u001b[39;49m\u001b[39mFailed to find instances\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m    400\u001b[0m \u001b[39mif\u001b[39;00m result \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    401\u001b[0m     \u001b[39mreturn\u001b[39;00m result\n",
      "File \u001b[1;32mc:\\Users\\57859\\.conda\\envs\\MachineLearning\\lib\\site-packages\\wapi\\curves.py:53\u001b[0m, in \u001b[0;36mBaseCurve._load_data\u001b[1;34m(self, url, failmsg, urlbase)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[39mif\u001b[39;00m urlbase \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m     urlbase \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_session\u001b[39m.\u001b[39murlbase\n\u001b[1;32m---> 53\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_session\u001b[39m.\u001b[39;49mdata_request(\u001b[39m'\u001b[39;49m\u001b[39mGET\u001b[39;49m\u001b[39m'\u001b[39;49m, urlbase, url)\n\u001b[0;32m     54\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_last_response \u001b[39m=\u001b[39m response\n\u001b[0;32m     55\u001b[0m \u001b[39mif\u001b[39;00m response\u001b[39m.\u001b[39mstatus_code \u001b[39m==\u001b[39m \u001b[39m200\u001b[39m:\n",
      "File \u001b[1;32mc:\\Users\\57859\\.conda\\envs\\MachineLearning\\lib\\site-packages\\wapi\\session.py:456\u001b[0m, in \u001b[0;36mSession.data_request\u001b[1;34m(self, req_type, urlbase, url, data, rawdata, authval, stream, retries)\u001b[0m\n\u001b[0;32m    454\u001b[0m timeout \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m    455\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 456\u001b[0m     res \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_session\u001b[39m.\u001b[39;49mrequest(method\u001b[39m=\u001b[39;49mreq_type, url\u001b[39m=\u001b[39;49mlongurl, data\u001b[39m=\u001b[39;49mdatabytes,\n\u001b[0;32m    457\u001b[0m                                 headers\u001b[39m=\u001b[39;49mheaders, auth\u001b[39m=\u001b[39;49mauthval, stream\u001b[39m=\u001b[39;49mstream, timeout\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtimeout)\n\u001b[0;32m    458\u001b[0m \u001b[39mexcept\u001b[39;00m requests\u001b[39m.\u001b[39mexceptions\u001b[39m.\u001b[39mTimeout \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    459\u001b[0m     timeout \u001b[39m=\u001b[39m e\n",
      "File \u001b[1;32mc:\\Users\\57859\\.conda\\envs\\MachineLearning\\lib\\site-packages\\requests\\sessions.py:587\u001b[0m, in \u001b[0;36mSession.request\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    582\u001b[0m send_kwargs \u001b[39m=\u001b[39m {\n\u001b[0;32m    583\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mtimeout\u001b[39m\u001b[39m\"\u001b[39m: timeout,\n\u001b[0;32m    584\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mallow_redirects\u001b[39m\u001b[39m\"\u001b[39m: allow_redirects,\n\u001b[0;32m    585\u001b[0m }\n\u001b[0;32m    586\u001b[0m send_kwargs\u001b[39m.\u001b[39mupdate(settings)\n\u001b[1;32m--> 587\u001b[0m resp \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msend(prep, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49msend_kwargs)\n\u001b[0;32m    589\u001b[0m \u001b[39mreturn\u001b[39;00m resp\n",
      "File \u001b[1;32mc:\\Users\\57859\\.conda\\envs\\MachineLearning\\lib\\site-packages\\requests\\sessions.py:701\u001b[0m, in \u001b[0;36mSession.send\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    698\u001b[0m start \u001b[39m=\u001b[39m preferred_clock()\n\u001b[0;32m    700\u001b[0m \u001b[39m# Send the request\u001b[39;00m\n\u001b[1;32m--> 701\u001b[0m r \u001b[39m=\u001b[39m adapter\u001b[39m.\u001b[39;49msend(request, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m    703\u001b[0m \u001b[39m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[0;32m    704\u001b[0m elapsed \u001b[39m=\u001b[39m preferred_clock() \u001b[39m-\u001b[39m start\n",
      "File \u001b[1;32mc:\\Users\\57859\\.conda\\envs\\MachineLearning\\lib\\site-packages\\requests\\adapters.py:489\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    487\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m    488\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m chunked:\n\u001b[1;32m--> 489\u001b[0m         resp \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39;49murlopen(\n\u001b[0;32m    490\u001b[0m             method\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mmethod,\n\u001b[0;32m    491\u001b[0m             url\u001b[39m=\u001b[39;49murl,\n\u001b[0;32m    492\u001b[0m             body\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mbody,\n\u001b[0;32m    493\u001b[0m             headers\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mheaders,\n\u001b[0;32m    494\u001b[0m             redirect\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m    495\u001b[0m             assert_same_host\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m    496\u001b[0m             preload_content\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m    497\u001b[0m             decode_content\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m    498\u001b[0m             retries\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmax_retries,\n\u001b[0;32m    499\u001b[0m             timeout\u001b[39m=\u001b[39;49mtimeout,\n\u001b[0;32m    500\u001b[0m         )\n\u001b[0;32m    502\u001b[0m     \u001b[39m# Send the request.\u001b[39;00m\n\u001b[0;32m    503\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    504\u001b[0m         \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(conn, \u001b[39m\"\u001b[39m\u001b[39mproxy_pool\u001b[39m\u001b[39m\"\u001b[39m):\n",
      "File \u001b[1;32mc:\\Users\\57859\\.conda\\envs\\MachineLearning\\lib\\site-packages\\urllib3\\connectionpool.py:703\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    700\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_prepare_proxy(conn)\n\u001b[0;32m    702\u001b[0m \u001b[39m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[1;32m--> 703\u001b[0m httplib_response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_request(\n\u001b[0;32m    704\u001b[0m     conn,\n\u001b[0;32m    705\u001b[0m     method,\n\u001b[0;32m    706\u001b[0m     url,\n\u001b[0;32m    707\u001b[0m     timeout\u001b[39m=\u001b[39;49mtimeout_obj,\n\u001b[0;32m    708\u001b[0m     body\u001b[39m=\u001b[39;49mbody,\n\u001b[0;32m    709\u001b[0m     headers\u001b[39m=\u001b[39;49mheaders,\n\u001b[0;32m    710\u001b[0m     chunked\u001b[39m=\u001b[39;49mchunked,\n\u001b[0;32m    711\u001b[0m )\n\u001b[0;32m    713\u001b[0m \u001b[39m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[0;32m    714\u001b[0m \u001b[39m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[0;32m    715\u001b[0m \u001b[39m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[0;32m    716\u001b[0m \u001b[39m# mess.\u001b[39;00m\n\u001b[0;32m    717\u001b[0m response_conn \u001b[39m=\u001b[39m conn \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m release_conn \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\57859\\.conda\\envs\\MachineLearning\\lib\\site-packages\\urllib3\\connectionpool.py:449\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    444\u001b[0m             httplib_response \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39mgetresponse()\n\u001b[0;32m    445\u001b[0m         \u001b[39mexcept\u001b[39;00m \u001b[39mBaseException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    446\u001b[0m             \u001b[39m# Remove the TypeError from the exception chain in\u001b[39;00m\n\u001b[0;32m    447\u001b[0m             \u001b[39m# Python 3 (including for exceptions like SystemExit).\u001b[39;00m\n\u001b[0;32m    448\u001b[0m             \u001b[39m# Otherwise it looks like a bug in the code.\u001b[39;00m\n\u001b[1;32m--> 449\u001b[0m             six\u001b[39m.\u001b[39;49mraise_from(e, \u001b[39mNone\u001b[39;49;00m)\n\u001b[0;32m    450\u001b[0m \u001b[39mexcept\u001b[39;00m (SocketTimeout, BaseSSLError, SocketError) \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    451\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_raise_timeout(err\u001b[39m=\u001b[39me, url\u001b[39m=\u001b[39murl, timeout_value\u001b[39m=\u001b[39mread_timeout)\n",
      "File \u001b[1;32m<string>:3\u001b[0m, in \u001b[0;36mraise_from\u001b[1;34m(value, from_value)\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\57859\\.conda\\envs\\MachineLearning\\lib\\site-packages\\urllib3\\connectionpool.py:444\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    441\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[0;32m    442\u001b[0m     \u001b[39m# Python 3\u001b[39;00m\n\u001b[0;32m    443\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 444\u001b[0m         httplib_response \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39;49mgetresponse()\n\u001b[0;32m    445\u001b[0m     \u001b[39mexcept\u001b[39;00m \u001b[39mBaseException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    446\u001b[0m         \u001b[39m# Remove the TypeError from the exception chain in\u001b[39;00m\n\u001b[0;32m    447\u001b[0m         \u001b[39m# Python 3 (including for exceptions like SystemExit).\u001b[39;00m\n\u001b[0;32m    448\u001b[0m         \u001b[39m# Otherwise it looks like a bug in the code.\u001b[39;00m\n\u001b[0;32m    449\u001b[0m         six\u001b[39m.\u001b[39mraise_from(e, \u001b[39mNone\u001b[39;00m)\n",
      "File \u001b[1;32mc:\\Users\\57859\\.conda\\envs\\MachineLearning\\lib\\http\\client.py:1348\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1346\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m   1347\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 1348\u001b[0m         response\u001b[39m.\u001b[39;49mbegin()\n\u001b[0;32m   1349\u001b[0m     \u001b[39mexcept\u001b[39;00m \u001b[39mConnectionError\u001b[39;00m:\n\u001b[0;32m   1350\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclose()\n",
      "File \u001b[1;32mc:\\Users\\57859\\.conda\\envs\\MachineLearning\\lib\\http\\client.py:316\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    314\u001b[0m \u001b[39m# read until we get a non-100 response\u001b[39;00m\n\u001b[0;32m    315\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m--> 316\u001b[0m     version, status, reason \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_read_status()\n\u001b[0;32m    317\u001b[0m     \u001b[39mif\u001b[39;00m status \u001b[39m!=\u001b[39m CONTINUE:\n\u001b[0;32m    318\u001b[0m         \u001b[39mbreak\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\57859\\.conda\\envs\\MachineLearning\\lib\\http\\client.py:277\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    276\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_read_status\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m--> 277\u001b[0m     line \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfp\u001b[39m.\u001b[39;49mreadline(_MAXLINE \u001b[39m+\u001b[39;49m \u001b[39m1\u001b[39;49m), \u001b[39m\"\u001b[39m\u001b[39miso-8859-1\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m    278\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(line) \u001b[39m>\u001b[39m _MAXLINE:\n\u001b[0;32m    279\u001b[0m         \u001b[39mraise\u001b[39;00m LineTooLong(\u001b[39m\"\u001b[39m\u001b[39mstatus line\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\57859\\.conda\\envs\\MachineLearning\\lib\\socket.py:669\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    667\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[0;32m    668\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 669\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sock\u001b[39m.\u001b[39;49mrecv_into(b)\n\u001b[0;32m    670\u001b[0m     \u001b[39mexcept\u001b[39;00m timeout:\n\u001b[0;32m    671\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_timeout_occurred \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\57859\\.conda\\envs\\MachineLearning\\lib\\ssl.py:1241\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[1;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[0;32m   1237\u001b[0m     \u001b[39mif\u001b[39;00m flags \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m   1238\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m   1239\u001b[0m           \u001b[39m\"\u001b[39m\u001b[39mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m\n\u001b[0;32m   1240\u001b[0m           \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m)\n\u001b[1;32m-> 1241\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mread(nbytes, buffer)\n\u001b[0;32m   1242\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m   1243\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39mrecv_into(buffer, nbytes, flags)\n",
      "File \u001b[1;32mc:\\Users\\57859\\.conda\\envs\\MachineLearning\\lib\\ssl.py:1099\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m   1097\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m   1098\u001b[0m     \u001b[39mif\u001b[39;00m buffer \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m-> 1099\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sslobj\u001b[39m.\u001b[39;49mread(\u001b[39mlen\u001b[39;49m, buffer)\n\u001b[0;32m   1100\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m   1101\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sslobj\u001b[39m.\u001b[39mread(\u001b[39mlen\u001b[39m)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#Lets test the model on data from a year before\n",
    "start2 = '2021-01-16'\n",
    "end2 = '2021-06-16'\n",
    "periods2=pd.date_range(start,end,freq='D')\n",
    "\n",
    "curves2 = ['con no1 mwh/h cet min15 a',\n",
    "'tt no1 con ec00 °c cet min15 f',\n",
    "'pro no1 wnd ec00 mwh/h cet min15 f',\n",
    "'pro no1 spv ec00 mwh/h cet min15 f'\n",
    "]\n",
    "\n",
    "df_watt2 = pd.DataFrame()\n",
    "for curve in curves2:\n",
    "    print(f\"Working with {curve}\")\n",
    "    curve = session.get_curve(name=curve)\n",
    "    curve_type = curve.curve_type\n",
    "    res = []\n",
    "    counter = 1\n",
    "    if curve_type == 'TIME_SERIES':\n",
    "        ts = curve.get_data(data_from=start, data_to=end, function='SAVERAGE', frequency='MIN15')\n",
    "        watt_temp = ts.to_pandas()\n",
    "        df_watt = pd.merge(df_watt, watt_temp, left_index=True, right_index=True, how='outer')\n",
    "        continue\n",
    "    \n",
    "    for j in range(len(periods)):\n",
    "        if j == len(periods)-1:\n",
    "            continue\n",
    "        if curve_type == 'TAGGED_INSTANCES':\n",
    "            ts_list = curve.search_instances(issue_date_from=periods[j],\n",
    "                                 issue_date_to=periods[j+1],\n",
    "                                 with_data=True, function='SAVERAGE', frequency='MIN15', tags=['Avg'])\n",
    "        else: #curve_type == 'INSTANCES'\n",
    "            ts_list = curve.search_instances(issue_date_from=periods[j],\n",
    "                                 issue_date_to=periods[j+1],\n",
    "                                 with_data=True, function='SAVERAGE', frequency='MIN15')\n",
    "        # check if ts_list is empty and if yes, jump over that timestamp\n",
    "        if not ts_list:\n",
    "            continue\n",
    "        pd_s = ts_list[0].to_pandas()\n",
    "        if pd_s.empty:\n",
    "            continue\n",
    "        pd_s = pd_s.sort_index(ascending=True)\n",
    "        pd_s = pd_s[:24]\n",
    "        res.append(pd_s)\n",
    "        print(f'Done {counter} out of {len(periods)}')\n",
    "        counter += 1\n",
    "\n",
    "    for k in range(len(res)):\n",
    "        if k == 0:\n",
    "            watt = pd.Series(res[k])\n",
    "        else:\n",
    "            watt = pd.concat([watt, res[k]])\n",
    "    watt_temp2 = watt.rename(curve)\n",
    "    df_watt2 = pd.merge(df_watt2, watt_temp2, left_index=True, right_index=True, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cons_actual</th>\n",
       "      <th>temp_forecast</th>\n",
       "      <th>wind_forecast</th>\n",
       "      <th>solar_forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-06-15 04:45:00+02:00</th>\n",
       "      <td>2367.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>159.601699</td>\n",
       "      <td>0.005099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-15 05:00:00+02:00</th>\n",
       "      <td>2454.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>156.741453</td>\n",
       "      <td>0.036253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-15 05:15:00+02:00</th>\n",
       "      <td>2454.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>156.741453</td>\n",
       "      <td>0.036253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-15 05:30:00+02:00</th>\n",
       "      <td>2454.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>156.741453</td>\n",
       "      <td>0.036253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-15 05:45:00+02:00</th>\n",
       "      <td>2454.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>156.741453</td>\n",
       "      <td>0.036253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           cons_actual  temp_forecast  wind_forecast  \\\n",
       "2021-06-15 04:45:00+02:00       2367.0            8.8     159.601699   \n",
       "2021-06-15 05:00:00+02:00       2454.0            9.5     156.741453   \n",
       "2021-06-15 05:15:00+02:00       2454.0            9.5     156.741453   \n",
       "2021-06-15 05:30:00+02:00       2454.0            9.5     156.741453   \n",
       "2021-06-15 05:45:00+02:00       2454.0            9.5     156.741453   \n",
       "\n",
       "                           solar_forecast  \n",
       "2021-06-15 04:45:00+02:00        0.005099  \n",
       "2021-06-15 05:00:00+02:00        0.036253  \n",
       "2021-06-15 05:15:00+02:00        0.036253  \n",
       "2021-06-15 05:30:00+02:00        0.036253  \n",
       "2021-06-15 05:45:00+02:00        0.036253  "
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_watt2.columns = [\"cons_actual\", \"temp_forecast\", \"wind_forecast\", \"solar_forecast\"]\n",
    "df_watt2.dropna(inplace=True)\n",
    "df_watt2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cons_actual</th>\n",
       "      <th>temp_forecast</th>\n",
       "      <th>wind_forecast</th>\n",
       "      <th>solar_forecast</th>\n",
       "      <th>Day sin</th>\n",
       "      <th>Day cos</th>\n",
       "      <th>Week sin</th>\n",
       "      <th>Week cos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-16 00:00:00+01:00</th>\n",
       "      <td>6472.0</td>\n",
       "      <td>-11.7</td>\n",
       "      <td>35.756445</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>0.982566</td>\n",
       "      <td>-0.185912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16 00:15:00+01:00</th>\n",
       "      <td>6472.0</td>\n",
       "      <td>-11.7</td>\n",
       "      <td>35.756445</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.950903e-01</td>\n",
       "      <td>0.980785</td>\n",
       "      <td>0.980785</td>\n",
       "      <td>-0.195090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16 00:30:00+01:00</th>\n",
       "      <td>6472.0</td>\n",
       "      <td>-11.7</td>\n",
       "      <td>35.756445</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.305262e-01</td>\n",
       "      <td>0.991445</td>\n",
       "      <td>0.978918</td>\n",
       "      <td>-0.204252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16 00:45:00+01:00</th>\n",
       "      <td>6472.0</td>\n",
       "      <td>-11.7</td>\n",
       "      <td>35.756445</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.540313e-02</td>\n",
       "      <td>0.997859</td>\n",
       "      <td>0.976966</td>\n",
       "      <td>-0.213396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16 01:00:00+01:00</th>\n",
       "      <td>6343.0</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>35.756445</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.583671e-13</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           cons_actual  temp_forecast  wind_forecast  \\\n",
       "2021-01-16 00:00:00+01:00       6472.0          -11.7      35.756445   \n",
       "2021-01-16 00:15:00+01:00       6472.0          -11.7      35.756445   \n",
       "2021-01-16 00:30:00+01:00       6472.0          -11.7      35.756445   \n",
       "2021-01-16 00:45:00+01:00       6472.0          -11.7      35.756445   \n",
       "2021-01-16 01:00:00+01:00       6343.0          -11.8      35.756445   \n",
       "\n",
       "                           solar_forecast       Day sin   Day cos  Week sin  \\\n",
       "2021-01-16 00:00:00+01:00             0.0 -2.588190e-01  0.965926  0.982566   \n",
       "2021-01-16 00:15:00+01:00             0.0 -1.950903e-01  0.980785  0.980785   \n",
       "2021-01-16 00:30:00+01:00             0.0 -1.305262e-01  0.991445  0.978918   \n",
       "2021-01-16 00:45:00+01:00             0.0 -6.540313e-02  0.997859  0.976966   \n",
       "2021-01-16 01:00:00+01:00             0.0  2.583671e-13  1.000000  0.974928   \n",
       "\n",
       "                           Week cos  \n",
       "2021-01-16 00:00:00+01:00 -0.185912  \n",
       "2021-01-16 00:15:00+01:00 -0.195090  \n",
       "2021-01-16 00:30:00+01:00 -0.204252  \n",
       "2021-01-16 00:45:00+01:00 -0.213396  \n",
       "2021-01-16 01:00:00+01:00 -0.222521  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_watt2['Seconds'] = df_watt.index.map(pd.Timestamp.timestamp)\n",
    "day = 60*60*24\n",
    "year = 365.2425*day\n",
    "week = day*7\n",
    "df_watt2['Day sin'] = np.sin(df_watt2['Seconds'] * (2* np.pi / day))\n",
    "df_watt2['Day cos'] = np.cos(df_watt2['Seconds'] * (2 * np.pi / day))\n",
    "df_watt2['Week sin'] = np.sin(df_watt2['Seconds'] * (2 * np.pi / week))\n",
    "df_watt2['Week cos'] = np.cos(df_watt2['Seconds'] * (2 * np.pi / week))\n",
    "\n",
    "df_watt2=df_watt2.drop(['Seconds'], axis=1)\n",
    "df_watt2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainX shape == (3598, 10, 8).\n",
      "trainY shape == (3598, 1).\n"
     ]
    }
   ],
   "source": [
    "cols = list(df_watt)[0:8]\n",
    "#New dataframe with only training data - 4 columns\n",
    "df_for_training = df_watt[cols].astype(float)\n",
    "scaler = StandardScaler()\n",
    "scaler = scaler.fit(df_for_training)\n",
    "df_for_training_scaled = scaler.transform(df_for_training)\n",
    "\n",
    "#Empty lists to be populated using formatted training data\n",
    "trainX = []\n",
    "trainY = []\n",
    "\n",
    "n_future = 1   # Number of timesteps we want to look into the future based on the past timesteps.\n",
    "n_past = 10  # Number of past timesteps we want to use to predict the future.\n",
    "\n",
    "#Reformat input data into a shape: (n_samples x timesteps x n_features)\n",
    "#In my example, my df_for_training_scaled has a shape (12823, 5)\n",
    "#12823 refers to the number of data points and 5 refers to the columns (multi-variables).\n",
    "for i in range(n_past, len(df_for_training_scaled) - n_future +1):\n",
    "    trainX.append(df_for_training_scaled[i - n_past:i, 0:df_for_training.shape[1]])\n",
    "    trainY.append(df_for_training_scaled[i + n_future - 1:i + n_future, 0])\n",
    "\n",
    "trainX, trainY = np.array(trainX), np.array(trainY)\n",
    "\n",
    "print('trainX shape == {}.'.format(trainX.shape))\n",
    "print('trainY shape == {}.'.format(trainY.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 7ms/step\n"
     ]
    }
   ],
   "source": [
    "#Make prediction\n",
    "prediction = model.predict(trainX[-n_days_for_prediction:]) #shape = (n, 1) where n is the n_days_for_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Perform inverse transformation to rescale back to original range\n",
    "#Since we used 5 variables for transform, the inverse expects same dimensions\n",
    "#Therefore, let us copy our values 5 times and discard them after inverse transform\n",
    "prediction_copies = np.repeat(prediction, df_for_training.shape[1], axis=-1)\n",
    "y_pred_future = scaler.inverse_transform(prediction_copies)[:,0]\n",
    "\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.plot(df_watt.index[-n_days_for_prediction:], y_pred_future, label='prediction')\n",
    "plt.plot(df_watt['cons_actual'][-n_days_for_prediction:], label='cons_actual')\n",
    "#Beautify x-axis\n",
    "plt.xticks(rotation=90)\n",
    "plt.gcf().autofmt_xdate()\n",
    "myFmt = mdates.DateFormatter(\"%c\")\n",
    "plt.gca().xaxis.set_major_formatter(myFmt)\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nope"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('MachineLearning')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "7d0f94619f62d23bb456f14bb24951151fc6964c45836f017699c6e7a624aa05"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}