Example_1_Quick_Functions.ipynb

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      "source": [
        "# *Electricity-Data-Pipeline*: Example 1\r\n",
        "_Click_ [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/desenk/Electricity-Data-Pipeline/blob/master/Example_1_Quick_Functions.ipynb) _to open this notebook in Google Colab_\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s1ygApu4yP60"
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      "source": [
        "## 1. Clone the Electricity-Data-Pipeline project and download all required packages"
      ]
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/"
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      "source": [
        "#Please comment out if you are NOT using Google Colab or Jupyter Notebook\r\n",
        "!git clone https://github.com/desenk/Electricity-Data-Pipeline.git\r\n",
        "%cd ./Electricity-Data-Pipeline\r\n",
        "!pip install -r requirements.txt"
      ],
      "execution_count": null,
      "outputs": [
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          "text": [
            "Cloning into 'Electricity-Data-Pipeline'...\n",
            "remote: Enumerating objects: 211, done.\u001b[K\n",
            "remote: Counting objects: 100% (211/211), done.\u001b[K\n",
            "remote: Compressing objects: 100% (108/108), done.\u001b[K\n",
            "remote: Total 211 (delta 107), reused 186 (delta 92), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (211/211), 337.31 KiB | 611.00 KiB/s, done.\n",
            "Resolving deltas: 100% (107/107), done.\n",
            "/content/Electricity-Data-Pipeline\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 4)) (4.2.6)\n",
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      "cell_type": "markdown",
      "metadata": {
        "id": "laG6shTgdxTN"
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      "source": [
        "## 2. Import the `Electricity-Data-Pipeline` modules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WU8SbGs4dxTN"
      },
      "source": [
        "from pipeline.BMRS_helpers import *\n",
        "from pipeline.range_import_helpers import *\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "markdown",
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      "source": [
        "## 3. Get your API key\n",
        "Please ensure that you obtain an API key from Elexon following the instructions below (adapted from [here](https://www.elexon.co.uk/documents/training-guidance/bsc-guidance-notes/bmrs-api-and-data-push-user-guide-2/)):\n",
        "\n",
        "1. Register [here](https://www.elexonportal.co.uk/registration/newuser?cachebust=3apx5qnzf9) as a new user \n",
        "> Check your inbox for the verification email\n",
        "2. Sign-in & Access your API Key\n",
        "> Log-in -> Click on `my profile` -> Copy the `scripting key`\n",
        "3. Paste the API Key in the `api_key.txt` file (NO quotes or other characters - just the copied key)\n",
        "> Google Colab: `folder icon` (on the left)>Electricity-Data-Pipeline>api_key.txt\n",
        "4. Check that your API Key is saved correctly\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9uqsy8ohzF5v"
      },
      "source": [
        "get_APIKey()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oolJIWEqdxTO"
      },
      "source": [
        "## 4. Import raw data using quick BMRS helper functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6nMTyz7TG6N6"
      },
      "source": [
        "### List of functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BtlVEYlhdxTO"
      },
      "source": [
        "Below is a list of all the `<Electricity-Data-Pipeline>` functions defined so far:\n",
        "\n",
        "_**Table 1:** List of the quick BMRS helper functions_\n",
        "\n",
        "`Electricity-Data-Pipeline` Quick Functions | Description | Resolution | Inputs \n",
        "------------ | ------------- | ------------ | ------------\n",
        "**`demand()`** | Rolling System Demand | 5 min | demand(start_date = 'YYYY-MM-DD', end_date = 'YYYY-MM-DD', save_to_csv = False)\n",
        "**`temperature()`** | Average Daily Temperature in Britain | Daily  | \"\n",
        "**`generation()`** | Half-hourly Generation by Fuel Type | Halfhourly (30 min) | \"\n",
        "**`frequency()`** | System Frequency | 15 sec | \"\n",
        "**`initial_demand_national()`** | Initial National Demand Out-turn | Halfhourly (30 min) | \"\n",
        "**`initial_demand_transmission()`** | Initial Transmission System Demand Out-turn | Halfhourly (30 min) | \"\n",
        "**`demand_forecast_national()`** | National Demand Forecast | Halfhourly (30 min) | \"\n",
        "**`demand_forecast_transmission()`** | Transmission System Demand Forecast | Halfhourly (30 min) | \"\n",
        "**`imbalance_volume()`** | Imbalance Volume | Halfhourly (30 min) | \"\n",
        "**`loss_of_load()`** | Loss of Load and De-rated Margin | Halfhourly (30 min) | \"\n",
        "**`imbalance_price()`** | Imbalance Price | Halfhourly (30 min) | \"\n",
        "**`derived_system_data()`** | Derived System Data | Halfhourly (30 min) | \"\n",
        "**`extract_data()`** | Uses BMRS data label and tries different methods | depends on dataset of choice | extract_data(report_name = 'TEMP', start_date = 'YYYY-MM-DD', end_date = 'YYYY-MM-DD', save_to_csv = True)\n",
        "\n",
        "_______________________________________________\n",
        "\n",
        "\n",
        "_**Table 2:** List of the data extractions functions for a week or longer periods._\n",
        "\n",
        "`Electricity-Data-Pipeline` Function for Weekly/Long-term Imports | Description | Range | Inputs \n",
        "------------ | ------------- | ------------ | ------------\n",
        "**`extract_data_weekly()`** | Extracts data for a week from the start_date using the function names from the table above| Fixed - Weekly | extract_data_weekly(func_name = demand , start_date = 'YYYY-MM-DD', save_to_csv = True)\n",
        "**`extract_data_range()`** | Extracts data for long timeframes | Variable  | extract_data_range(func_name = temperature, start_date = 'YYYY-MM-DD', end_date =  'YYYY-MM-DD', save_to_csv = False)\n",
        "**`extract_data_range_with_BMRS_label()`** | Same as above but using BMRS report names rather than the function names from the table above | Variable | data_extract_range_with_BMRS_label(report_name = 'TEMP', start_date = 'YYYY-MM-DD', end_date =  'YYYY-MM-DD', save_to_csv = False)"
      ]
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      "source": [
        "Here, we demonstrate the quick functions used for short-term data import (See Table 1).\n",
        "Please note that these functions are subject to capping and time-out limits imposed by the data provider. Please look at the range import functions to overcome this (See Table 2)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nI6zakEFdxTP"
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      "source": [
        "### Demand example with default dates"
      ]
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      "source": [
        "demand = demand()\n",
        "demand"
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              "    recordType          settDate  ... fuelTypeGeneration activeFlag\n",
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        "outputId": "806fd746-5b91-4f2c-8784-da5ec551d491"
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      "source": [
        "%matplotlib inline\n",
        "plt.style.use('seaborn')\n",
        "plt.plot(demand['fuelTypeGeneration'])"
      ],
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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eHUw0gYPdxTQ"
      },
      "source": [
        "### Generation example with custom dates & save_to_csv option enabled"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9FbkBmudxTR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "8ddcb73b-ec75-40bc-c04c-dcfd3e997cd0"
      },
      "source": [
        "generation_default = generation() #default dates \n",
        "generation_default.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>recordType</th>\n",
              "      <th>startTimeOfHalfHrPeriod</th>\n",
              "      <th>settlementPeriod</th>\n",
              "      <th>ccgt</th>\n",
              "      <th>oil</th>\n",
              "      <th>coal</th>\n",
              "      <th>nuclear</th>\n",
              "      <th>wind</th>\n",
              "      <th>ps</th>\n",
              "      <th>npshyd</th>\n",
              "      <th>ocgt</th>\n",
              "      <th>other</th>\n",
              "      <th>intfr</th>\n",
              "      <th>intirl</th>\n",
              "      <th>intned</th>\n",
              "      <th>intew</th>\n",
              "      <th>biomass</th>\n",
              "      <th>intnem</th>\n",
              "      <th>intelec</th>\n",
              "      <th>intifa2</th>\n",
              "      <th>intnsl</th>\n",
              "      <th>activeFlag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[[[FUELHH]]]</td>\n",
              "      <td>[[[2020-03-24]]]</td>\n",
              "      <td>[[[1]]]</td>\n",
              "      <td>[[[6094]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[4319]]]</td>\n",
              "      <td>[[[9415]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[424]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[122]]]</td>\n",
              "      <td>[[[1510]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[758]]]</td>\n",
              "      <td>[[[128]]]</td>\n",
              "      <td>[[[1728]]]</td>\n",
              "      <td>[[[756]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[Y]]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[[[FUELHH]]]</td>\n",
              "      <td>[[[2020-03-24]]]</td>\n",
              "      <td>[[[2]]]</td>\n",
              "      <td>[[[6126]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[4319]]]</td>\n",
              "      <td>[[[9681]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[423]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[123]]]</td>\n",
              "      <td>[[[1508]]]</td>\n",
              "      <td>[[[66]]]</td>\n",
              "      <td>[[[758]]]</td>\n",
              "      <td>[[[118]]]</td>\n",
              "      <td>[[[1735]]]</td>\n",
              "      <td>[[[756]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[Y]]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[[[FUELHH]]]</td>\n",
              "      <td>[[[2020-03-24]]]</td>\n",
              "      <td>[[[3]]]</td>\n",
              "      <td>[[[5867]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[4246]]]</td>\n",
              "      <td>[[[9806]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[426]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[124]]]</td>\n",
              "      <td>[[[1508]]]</td>\n",
              "      <td>[[[74]]]</td>\n",
              "      <td>[[[758]]]</td>\n",
              "      <td>[[[270]]]</td>\n",
              "      <td>[[[1730]]]</td>\n",
              "      <td>[[[756]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[Y]]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[[[FUELHH]]]</td>\n",
              "      <td>[[[2020-03-24]]]</td>\n",
              "      <td>[[[4]]]</td>\n",
              "      <td>[[[5452]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[4149]]]</td>\n",
              "      <td>[[[9926]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[425]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[125]]]</td>\n",
              "      <td>[[[1508]]]</td>\n",
              "      <td>[[[22]]]</td>\n",
              "      <td>[[[758]]]</td>\n",
              "      <td>[[[342]]]</td>\n",
              "      <td>[[[1722]]]</td>\n",
              "      <td>[[[756]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[Y]]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[[[FUELHH]]]</td>\n",
              "      <td>[[[2020-03-24]]]</td>\n",
              "      <td>[[[5]]]</td>\n",
              "      <td>[[[5124]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[4143]]]</td>\n",
              "      <td>[[[10053]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[422]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[125]]]</td>\n",
              "      <td>[[[1410]]]</td>\n",
              "      <td>[[[66]]]</td>\n",
              "      <td>[[[710]]]</td>\n",
              "      <td>[[[476]]]</td>\n",
              "      <td>[[[1729]]]</td>\n",
              "      <td>[[[708]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[Y]]]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     recordType startTimeOfHalfHrPeriod  ...   intnsl activeFlag\n",
              "0  [[[FUELHH]]]        [[[2020-03-24]]]  ...  [[[0]]]    [[[Y]]]\n",
              "1  [[[FUELHH]]]        [[[2020-03-24]]]  ...  [[[0]]]    [[[Y]]]\n",
              "2  [[[FUELHH]]]        [[[2020-03-24]]]  ...  [[[0]]]    [[[Y]]]\n",
              "3  [[[FUELHH]]]        [[[2020-03-24]]]  ...  [[[0]]]    [[[Y]]]\n",
              "4  [[[FUELHH]]]        [[[2020-03-24]]]  ...  [[[0]]]    [[[Y]]]\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2C4_t_YfdxTR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "73a83e7c-8227-4a19-c79c-872eb11eebb8"
      },
      "source": [
        "#function(start_date = '2020-03-28', end_date = '2020-03-31, save_to_csv = True)\n",
        "generation_custom = generation(start_date = '2020-03-28', end_date = '2020-03-31', save_to_csv = True)\n",
        "generation_custom"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "saved FUELHH_2020-03-28_2020-03-31.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>recordType</th>\n",
              "      <th>startTimeOfHalfHrPeriod</th>\n",
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              "      <td>[[[4354]]]</td>\n",
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              "      <td>[[[FUELHH]]]</td>\n",
              "      <td>[[[2020-03-28]]]</td>\n",
              "      <td>[[[3]]]</td>\n",
              "      <td>[[[5705]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[4355]]]</td>\n",
              "      <td>[[[7032]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[510]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[150]]]</td>\n",
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              "      <td>[[[202]]]</td>\n",
              "      <td>[[[682]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[2036]]]</td>\n",
              "      <td>[[[668]]]</td>\n",
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              "      <th>3</th>\n",
              "      <td>[[[FUELHH]]]</td>\n",
              "      <td>[[[2020-03-28]]]</td>\n",
              "      <td>[[[4]]]</td>\n",
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              "      <td>[[[0]]]</td>\n",
              "      <td>[[[508]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
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              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>[[[FUELHH]]]</td>\n",
              "      <td>[[[2020-03-31]]]</td>\n",
              "      <td>[[[44]]]</td>\n",
              "      <td>[[[16922]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[337]]]</td>\n",
              "      <td>[[[4193]]]</td>\n",
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              "      <td>[[[0]]]</td>\n",
              "      <td>[[[662]]]</td>\n",
              "      <td>[[[1]]]</td>\n",
              "      <td>[[[160]]]</td>\n",
              "      <td>[[[2004]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[1056]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
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              "      <td>[[[1000]]]</td>\n",
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              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[Y]]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>[[[FUELHH]]]</td>\n",
              "      <td>[[[2020-03-31]]]</td>\n",
              "      <td>[[[45]]]</td>\n",
              "      <td>[[[16098]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[265]]]</td>\n",
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              "      <td>[[[3193]]]</td>\n",
              "      <td>[[[58]]]</td>\n",
              "      <td>[[[565]]]</td>\n",
              "      <td>[[[1]]]</td>\n",
              "      <td>[[[159]]]</td>\n",
              "      <td>[[[2004]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[1000]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[2107]]]</td>\n",
              "      <td>[[[1000]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[Y]]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>[[[FUELHH]]]</td>\n",
              "      <td>[[[2020-03-31]]]</td>\n",
              "      <td>[[[46]]]</td>\n",
              "      <td>[[[15198]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[4191]]]</td>\n",
              "      <td>[[[3260]]]</td>\n",
              "      <td>[[[8]]]</td>\n",
              "      <td>[[[559]]]</td>\n",
              "      <td>[[[1]]]</td>\n",
              "      <td>[[[159]]]</td>\n",
              "      <td>[[[2004]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[1000]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[1854]]]</td>\n",
              "      <td>[[[1000]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[Y]]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>[[[FUELHH]]]</td>\n",
              "      <td>[[[2020-03-31]]]</td>\n",
              "      <td>[[[47]]]</td>\n",
              "      <td>[[[14305]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[4188]]]</td>\n",
              "      <td>[[[3263]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[446]]]</td>\n",
              "      <td>[[[1]]]</td>\n",
              "      <td>[[[159]]]</td>\n",
              "      <td>[[[1906]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[952]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[1439]]]</td>\n",
              "      <td>[[[952]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[Y]]]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>[[[FUELHH]]]</td>\n",
              "      <td>[[[2020-03-31]]]</td>\n",
              "      <td>[[[48]]]</td>\n",
              "      <td>[[[13190]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[4193]]]</td>\n",
              "      <td>[[[3127]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[440]]]</td>\n",
              "      <td>[[[1]]]</td>\n",
              "      <td>[[[159]]]</td>\n",
              "      <td>[[[1904]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[950]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[1446]]]</td>\n",
              "      <td>[[[952]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[0]]]</td>\n",
              "      <td>[[[Y]]]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>190 rows × 22 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       recordType startTimeOfHalfHrPeriod  ...   intnsl activeFlag\n",
              "0    [[[FUELHH]]]        [[[2020-03-28]]]  ...  [[[0]]]    [[[Y]]]\n",
              "1    [[[FUELHH]]]        [[[2020-03-28]]]  ...  [[[0]]]    [[[Y]]]\n",
              "2    [[[FUELHH]]]        [[[2020-03-28]]]  ...  [[[0]]]    [[[Y]]]\n",
              "3    [[[FUELHH]]]        [[[2020-03-28]]]  ...  [[[0]]]    [[[Y]]]\n",
              "4    [[[FUELHH]]]        [[[2020-03-28]]]  ...  [[[0]]]    [[[Y]]]\n",
              "..            ...                     ...  ...      ...        ...\n",
              "185  [[[FUELHH]]]        [[[2020-03-31]]]  ...  [[[0]]]    [[[Y]]]\n",
              "186  [[[FUELHH]]]        [[[2020-03-31]]]  ...  [[[0]]]    [[[Y]]]\n",
              "187  [[[FUELHH]]]        [[[2020-03-31]]]  ...  [[[0]]]    [[[Y]]]\n",
              "188  [[[FUELHH]]]        [[[2020-03-31]]]  ...  [[[0]]]    [[[Y]]]\n",
              "189  [[[FUELHH]]]        [[[2020-03-31]]]  ...  [[[0]]]    [[[Y]]]\n",
              "\n",
              "[190 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5eDFATkdxTR"
      },
      "source": [
        "### Here is the rest of the functions, for you to try out:\n",
        "\n",
        "```\n",
        "demand()\n",
        "temperature()\n",
        "generation()\n",
        "frequency()\n",
        "loss_of_load()\n",
        "initial_demand_national()\n",
        "initial_demand_transmission()\n",
        "demand_forecast_national()\n",
        "demand_forecast_transmission()\n",
        "imbalance_volume()\n",
        "imbalance_price()\n",
        "derived_system_data()\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ew-uLslLdxTR"
      },
      "source": [
        "### Use case:\n",
        "To see how ```Electricity-Data-Pipeline``` was employed for assesing the impact of the March 2020 COVID-19 lockdown on the electricity system, please visit our publication below:\n",
        "> Kirli, Desen; Parzen, Maximilian; Kiprakis, Aristides. 2021. \"Impact of the COVID-19 Lockdown on the Electricity System of Great Britain: A Study on Energy Demand, Generation, Pricing and Grid Stability\" Energies 14, no. 3: 635. [https://doi.org/10.3390/en14030635](https://doi.org/10.3390/en14030635)\n",
        "\n",
        "All data used in this paper lives here:\n",
        "> Kirli, Desen; Kiprakis, Aristides; Parzen, Max. (2021). Impact of the COVID-19 Lockdown on the Electricity System of Great Britain: A Study on Energy Demand, Generation, Pricing and Grid Stability, 2019-2020 [dataset]. University of Edinburgh. School of Engineering. Institute for Energy Systems. [https://doi.org/10.7488/ds/2979](https://doi.org/10.7488/ds/2979)."
      ]
    }
  ]
}