tepco.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Electricity time series data from Tepco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/annette/.local/share/virtualenvs/CODE-iLw4NDUo/lib/python3.8/site-packages/IPython/core/magics/pylab.py:159: UserWarning:\n",
      "\n",
      "pylab import has clobbered these variables: ['datetime']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from io import StringIO\n",
    "import requests\n",
    "import json\n",
    "import plotly.express as px\n",
    "import ipywidgets as widgets\n",
    "from datetime import datetime\n",
    "import pytz\n",
    "import matplotlib.dates as mdates\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "General time series tutorial\n",
    "https://jakevdp.github.io/PythonDataScienceHandbook/03.11-working-with-time-series.html\n",
    "https://www.dataquest.io/blog/tutorial-time-series-analysis-with-pandas/\n",
    "\n",
    "Kaggle forcasting\n",
    "https://www.kaggle.com/apoorvabhide/energy-consumption-time-series-forecasting-in-r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df19 = pd.read_csv(\"data/juyo-2019.csv\")\n",
    "df20 = pd.read_csv(\"data/juyo-2020.csv\")\n",
    "df19[\"daytime\"] = pd.to_datetime(df19[\"DATE\"]+\" \"+df19[\"TIME\"], format=\"%Y/%m/%d %H:%M\")\n",
    "df20[\"daytime\"] = pd.to_datetime(df20[\"DATE\"]+\" \"+df20[\"TIME\"], format=\"%Y/%m/%d %H:%M\")\n",
    "df20[\"実績(万kW)\"]\n",
    "\n",
    "df20 = df20.set_index('daytime')\n",
    "df19 = df19.set_index('daytime')\n",
    "\n",
    "df = pd.concat([df19, df20], sort=True)\n",
    "\n",
    "# \"実績(万kW)\" = demand in 10MW\n",
    "df = df.rename(columns={\"実績(万kW)\": \"demand\", \"TIME\": 'time', \"DATE\": \"date\"}, errors=\"raise\")\n",
    "# bring demand to MW (not 10MW)\n",
    "df[\"demand\"]=df[\"demand\"]*10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>demand</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daytime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:00:00</th>\n",
       "      <td>2019/1/1</td>\n",
       "      <td>0:00</td>\n",
       "      <td>30170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 01:00:00</th>\n",
       "      <td>2019/1/1</td>\n",
       "      <td>1:00</td>\n",
       "      <td>28490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 02:00:00</th>\n",
       "      <td>2019/1/1</td>\n",
       "      <td>2:00</td>\n",
       "      <td>27300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 03:00:00</th>\n",
       "      <td>2019/1/1</td>\n",
       "      <td>3:00</td>\n",
       "      <td>26360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 04:00:00</th>\n",
       "      <td>2019/1/1</td>\n",
       "      <td>4:00</td>\n",
       "      <td>25790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-27 19:00:00</th>\n",
       "      <td>2020/4/27</td>\n",
       "      <td>19:00</td>\n",
       "      <td>31160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-27 20:00:00</th>\n",
       "      <td>2020/4/27</td>\n",
       "      <td>20:00</td>\n",
       "      <td>29640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-27 21:00:00</th>\n",
       "      <td>2020/4/27</td>\n",
       "      <td>21:00</td>\n",
       "      <td>28440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-27 22:00:00</th>\n",
       "      <td>2020/4/27</td>\n",
       "      <td>22:00</td>\n",
       "      <td>27010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-27 23:00:00</th>\n",
       "      <td>2020/4/27</td>\n",
       "      <td>23:00</td>\n",
       "      <td>25580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11592 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          date   time  demand\n",
       "daytime                                      \n",
       "2019-01-01 00:00:00   2019/1/1   0:00   30170\n",
       "2019-01-01 01:00:00   2019/1/1   1:00   28490\n",
       "2019-01-01 02:00:00   2019/1/1   2:00   27300\n",
       "2019-01-01 03:00:00   2019/1/1   3:00   26360\n",
       "2019-01-01 04:00:00   2019/1/1   4:00   25790\n",
       "...                        ...    ...     ...\n",
       "2020-04-27 19:00:00  2020/4/27  19:00   31160\n",
       "2020-04-27 20:00:00  2020/4/27  20:00   29640\n",
       "2020-04-27 21:00:00  2020/4/27  21:00   28440\n",
       "2020-04-27 22:00:00  2020/4/27  22:00   27010\n",
       "2020-04-27 23:00:00  2020/4/27  23:00   25580\n",
       "\n",
       "[11592 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['month'] = df.index.month\n",
    "df['weekday'] = df.index.weekday\n",
    "df['year']= df.index.year\n",
    "#sns.lineplot(x=df, y=\"demand\", ci=95, data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>demand</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daytime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>4404120</td>\n",
       "      <td>144</td>\n",
       "      <td>504</td>\n",
       "      <td>290736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-09</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-04</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-05</th>\n",
       "      <td>1642910</td>\n",
       "      <td>144</td>\n",
       "      <td>24</td>\n",
       "      <td>96960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>456 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             demand  month  weekday    year\n",
       "daytime                                    \n",
       "2019-01-06  4404120    144      504  290736\n",
       "2019-01-07        0      0        0       0\n",
       "2019-01-08        0      0        0       0\n",
       "2019-01-09        0      0        0       0\n",
       "2019-01-10        0      0        0       0\n",
       "...             ...    ...      ...     ...\n",
       "2020-04-01        0      0        0       0\n",
       "2020-04-02        0      0        0       0\n",
       "2020-04-03        0      0        0       0\n",
       "2020-04-04        0      0        0       0\n",
       "2020-04-05  1642910    144       24   96960\n",
       "\n",
       "[456 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(11, 4)})\n",
    "df.head()\n",
    "daily = df.loc['2019-01-01':'2020-03-31'].resample('W').sum()\n",
    "df.loc['2019-03-01':'2020-04-02', 'demand']\n",
    "df19=df.loc['2019-03-01': '2019-04-02', 'demand']\n",
    "df20=df.loc['2020-03-01': '2020-04-02', 'demand']\n",
    "daily.tail()\n",
    "daily.resample('D').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "daytime\n",
       "2019-04-02 19:00:00    39300\n",
       "2019-04-02 20:00:00    38420\n",
       "2019-04-02 21:00:00    37190\n",
       "2019-04-02 22:00:00    35360\n",
       "2019-04-02 23:00:00    32920\n",
       "Name: demand, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df19.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>demand</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daytime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-27</th>\n",
       "      <td>724280</td>\n",
       "      <td>72</td>\n",
       "      <td>96</td>\n",
       "      <td>48480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-28</th>\n",
       "      <td>653340</td>\n",
       "      <td>72</td>\n",
       "      <td>120</td>\n",
       "      <td>48480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-29</th>\n",
       "      <td>781570</td>\n",
       "      <td>72</td>\n",
       "      <td>144</td>\n",
       "      <td>48480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-30</th>\n",
       "      <td>833830</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>48480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>809080</td>\n",
       "      <td>72</td>\n",
       "      <td>24</td>\n",
       "      <td>48480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            demand  month  weekday   year\n",
       "daytime                                  \n",
       "2020-03-27  724280     72       96  48480\n",
       "2020-03-28  653340     72      120  48480\n",
       "2020-03-29  781570     72      144  48480\n",
       "2020-03-30  833830     72        0  48480\n",
       "2020-03-31  809080     72       24  48480"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekly = df.loc['2019-01-01':'2020-03-31']\n",
    "\n",
    "#daily = daily.loc['2019-01-01':'2019-03-31', 'demand']\n",
    "weekly = weekly.resample('D').sum()\n",
    "#daily.loc['2019-01-01':'2019-04-02', 'demand']\n",
    "#df.loc['2019-03-01':'2020-04-02', 'demand']\n",
    "weekly.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(daily.loc['2020-01-01':'2020-04-30', 'demand'], marker='o', linestyle='-')\n",
    "ax.set_ylabel('Daily Consumption (GWh)')\n",
    "ax.set_title('Jan-Mar 2020 Electricity Consumption')\n",
    "# Set x-axis major ticks to weekly interval, on Mondays\n",
    "ax.xaxis.set_major_locator(mdates.WeekdayLocator(byweekday=mdates.MONDAY))\n",
    "# Format x-tick labels as 3-letter month name and day number\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'));\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ax = df.loc['2019', 'demand'].plot()\n",
    "# ax.set_ylabel('Daily Consumption (MWh)');\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(daily.loc['2019-01-01':'2019-03-31', 'demand'], marker='o', linestyle='-')\n",
    "ax.set_ylabel('Daily Consumption (GWh)')\n",
    "ax.set_title('Jan-Mar 2019 Electricity Consumption')\n",
    "# Set x-axis major ticks to weekly interval, on Mondays\n",
    "ax.xaxis.set_major_locator(mdates.WeekdayLocator(byweekday=mdates.MONDAY))\n",
    "# Format x-tick labels as 3-letter month name and day number\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'));\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df19=df.loc['2019-03-01':'2019-03-30', 'demand']\n",
    "df20=df.loc['2020-03-01':'2020-03-30', 'demand']\n",
    "df20\n",
    "by_weekday = df.groupby(df.index.dayofweek).mean()['demand']\n",
    "by_weekday.index = ['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun']\n",
    "by_weekday.plot(style=[':', '--', '-']);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df19=daily.loc['2019-03-01':'2019-03-30', 'demand']\n",
    "df20=daily.loc['2020-03-01':'2020-03-30', 'demand']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}