Data Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import xlrd\n",
    "import datetime\n",
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('Data.xlsx')\n",
    "#data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total rows: 8760\n"
     ]
    }
   ],
   "source": [
    "entries = \"Total rows: {0}\".format(len(data))\n",
    "print (entries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No                                     int64\n",
       "Date                          datetime64[ns]\n",
       "Hour                                   int64\n",
       "Load                                 float64\n",
       "Wind Speed (m/s)                     float64\n",
       "PV Production (kW)                   float64\n",
       "Total Wind Production (kW)           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>No</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Load</th>\n",
       "      <th>Wind Speed (m/s)</th>\n",
       "      <th>PV Production (kW)</th>\n",
       "      <th>Total Wind Production (kW)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4380.500000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>1206.993747</td>\n",
       "      <td>5.955160</td>\n",
       "      <td>465.103723</td>\n",
       "      <td>509.818825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2528.938512</td>\n",
       "      <td>6.922582</td>\n",
       "      <td>273.366649</td>\n",
       "      <td>0.895772</td>\n",
       "      <td>772.804194</td>\n",
       "      <td>298.686848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>829.756000</td>\n",
       "      <td>3.266542</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.059908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2190.750000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>1005.100000</td>\n",
       "      <td>5.271204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>298.617511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4380.500000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>1142.628000</td>\n",
       "      <td>5.942433</td>\n",
       "      <td>12.236907</td>\n",
       "      <td>442.396313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6570.250000</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>1317.428000</td>\n",
       "      <td>6.665088</td>\n",
       "      <td>617.963779</td>\n",
       "      <td>713.364055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8760.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2643.228000</td>\n",
       "      <td>8.646830</td>\n",
       "      <td>3505.873715</td>\n",
       "      <td>1609.216590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                No         Hour         Load  Wind Speed (m/s)  \\\n",
       "count  8760.000000  8760.000000  8760.000000       8760.000000   \n",
       "mean   4380.500000    11.500000  1206.993747          5.955160   \n",
       "std    2528.938512     6.922582   273.366649          0.895772   \n",
       "min       1.000000     0.000000   829.756000          3.266542   \n",
       "25%    2190.750000     5.750000  1005.100000          5.271204   \n",
       "50%    4380.500000    11.500000  1142.628000          5.942433   \n",
       "75%    6570.250000    17.250000  1317.428000          6.665088   \n",
       "max    8760.000000    23.000000  2643.228000          8.646830   \n",
       "\n",
       "       PV Production (kW)  Total Wind Production (kW)  \n",
       "count         8760.000000                 8760.000000  \n",
       "mean           465.103723                  509.818825  \n",
       "std            772.804194                  298.686848  \n",
       "min              0.000000                   11.059908  \n",
       "25%              0.000000                  298.617511  \n",
       "50%             12.236907                  442.396313  \n",
       "75%            617.963779                  713.364055  \n",
       "max           3505.873715                 1609.216590  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#decomposition: anual, monthyly, weekly, daily\n",
    "#correlation\n",
    "#visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8760 entries, 0 to 8759\n",
      "Data columns (total 7 columns):\n",
      "No                            8760 non-null int64\n",
      "Date                          8760 non-null datetime64[ns]\n",
      "Hour                          8760 non-null int64\n",
      "Load                          8760 non-null float64\n",
      "Wind Speed (m/s)              8760 non-null float64\n",
      "PV Production (kW)            8760 non-null float64\n",
      "Total Wind Production (kW)    8760 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(4), int64(2)\n",
      "memory usage: 479.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2133229ac18>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data[['Date']]\n",
    "y = data[['Load']].rolling(24).mean()\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.xlabel('Date',fontsize=18)\n",
    "plt.ylabel('Demand (KWh)',fontsize=18)\n",
    "plt.plot(x,y,linewidth=3)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data[['Date']]\n",
    "y = data[['Load']].diff(24)\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.xlabel('Date',fontsize=18)\n",
    "plt.ylabel('Demand (KWh)',fontsize=18)\n",
    "plt.plot(x,y,linewidth=3)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data[['Date']]\n",
    "y = data[['Load']]\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.xlabel('Date',fontsize=18)\n",
    "plt.ylabel('Demand (KWh)',fontsize=18)\n",
    "plt.plot(x,y,linewidth=2)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import autocorrelation_plot\n",
    "from matplotlib.pyplot import figure\n",
    "plt.figure(linewidth=3, figsize=(20,15))\n",
    "autocorrelation_plot(data[['Load']]).plot();\n",
    "# Autocorrelation plots are often used for checking randomness in time series. This is done by computing autocorrelations for data values at varying time lags. If time series is random, such autocorrelations should be near zero for any and all time-lag separations. If time series is non-random then one or more of the autocorrelations will be significantly non-zero. The horizontal lines displayed in the plot correspond to 95% and 99% confidence bands. The dashed line is 99% confidence band. See the Wikipedia entry for more about autocorrelation plots.\n",
    "# https://machinelearningmastery.com/time-series-data-visualization-with-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot\n",
    "series = data['Load']\n",
    "data['Load'].hist(linewidth=2,figsize=(20,15))\n",
    "plt.ylabel('Distribution',fontsize=18)\n",
    "plt.xlabel('Demand (KWh)',fontsize=18)\n",
    "pyplot.show()\n",
    "#A histogram groups values into bins, and the frequency or count of observations in each bin can provide insight into the underlying distribution of the observations.\n",
    "# Running the example shows a distribution that looks strongly Gaussian. The plotting function automatically selects the size of the bins based on the spread of values in the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "series.plot(kind='kde',linewidth=2,figsize=(20,15))\n",
    "plt.ylabel('Density',fontsize=18)\n",
    "plt.xlabel('Demand (KWh)',fontsize=18)\n",
    "pyplot.show()\n",
    "# We can get a better idea of the shape of the distribution of observations by using a density plot. This is like the histogram, except a function is used to fit the distribution of observations and a nice, smooth line is used to summarize this distribution.\n",
    "# Running the example creates a plot that provides a clearer summary of the distribution of observations. We can see that perhaps the distribution is a little asymmetrical and perhaps a little pointy to be Gaussian.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import lag_plot\n",
    "plt.figure(linewidth=2, figsize=(20,15))\n",
    "lag_plot(series)\n",
    "pyplot.show()\n",
    "#Time series modeling assumes a relationship between an observation and the previous observation. Previous observations in a time series are called lags, with the observation at the previous time step called lag1, the observation at two time steps ago lag2, and so on. A useful type of plot to explore the relationship between each observation and a lag of that observation is called the scatter plot. Pandas has a built-in function for exactly this called the lag plot. It plots the observation at time t on the x-axis and the lag1 observation (t-1) on the y-axis. If the points cluster along a diagonal line from the bottom-left to the top-right of the plot, it suggests a positive correlation relationship. If the points cluster along a diagonal line from the top-left to the bottom-right, it suggests a negative correlation relationship. Either relationship is good as they can be modeled. More points tighter in to the diagonal line suggests a stronger relationship and more spread from the line suggests a weaker relationship. A ball in the middle or a spread across the plot suggests a weak or no relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "#from pylab import rcParams\n",
    "#rcParams['figure.figsize'] = 14, 15\n",
    "#series = data['Load'].dropna()\n",
    "#result = seasonal_decompose(series, model='multiplicative', freq=365)###\n",
    "\n",
    "#result.plot()\n",
    "#pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#next: add features and plot accordingly\n",
    "# after next: group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Load'].max()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Load'].min()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Load'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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