Data Preparaion & Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e9988c63",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from lightgbm import LGBMRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error as RMSE\n",
    "from src.help_func import add_weatherFeature, preprocess_data\n",
    "\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "177bef6a",
   "metadata": {},
   "source": [
    "# Load Data & Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "ba1da8df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load\n",
    "path = './datathon-code/data'\n",
    "ercot_hourly = pd.read_csv(path+'/ercot_hourly_load.csv')\n",
    "ercot_hourly.fillna(method='backfill',inplace=True)\n",
    "weather_history = pd.read_csv(path+'/weather_history.csv')\n",
    "weather_forecast = pd.read_csv(path+'/weather_forecast.csv')\n",
    "\n",
    "\n",
    "# preprocess\n",
    "preprocess_data(ercot_hourly, weather_history.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "93d43081",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coast</th>\n",
       "      <th>East</th>\n",
       "      <th>Far West</th>\n",
       "      <th>North</th>\n",
       "      <th>North Central</th>\n",
       "      <th>South</th>\n",
       "      <th>South Central</th>\n",
       "      <th>West</th>\n",
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       "    <tr>\n",
       "      <th>Hour_Ending</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-01 07:00:00</th>\n",
       "      <td>7507.810731</td>\n",
       "      <td>850.313988</td>\n",
       "      <td>998.465500</td>\n",
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       "      <td>3728.400885</td>\n",
       "      <td>692.294503</td>\n",
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       "    <tr>\n",
       "      <th>2005-01-01 08:00:00</th>\n",
       "      <td>7257.549136</td>\n",
       "      <td>818.143264</td>\n",
       "      <td>987.532852</td>\n",
       "      <td>711.996586</td>\n",
       "      <td>7012.466197</td>\n",
       "      <td>1798.234153</td>\n",
       "      <td>3562.614723</td>\n",
       "      <td>675.176013</td>\n",
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       "    <tr>\n",
       "      <th>2005-01-01 09:00:00</th>\n",
       "      <td>6973.987199</td>\n",
       "      <td>783.396624</td>\n",
       "      <td>978.575662</td>\n",
       "      <td>687.881144</td>\n",
       "      <td>6712.807281</td>\n",
       "      <td>1712.758395</td>\n",
       "      <td>3373.868049</td>\n",
       "      <td>659.650453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01 10:00:00</th>\n",
       "      <td>6794.489296</td>\n",
       "      <td>760.639635</td>\n",
       "      <td>959.486947</td>\n",
       "      <td>673.419383</td>\n",
       "      <td>6502.378446</td>\n",
       "      <td>1651.210254</td>\n",
       "      <td>3239.047417</td>\n",
       "      <td>648.512894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01 11:00:00</th>\n",
       "      <td>6685.186677</td>\n",
       "      <td>743.371691</td>\n",
       "      <td>955.268590</td>\n",
       "      <td>664.792118</td>\n",
       "      <td>6440.559639</td>\n",
       "      <td>1627.872766</td>\n",
       "      <td>3176.859720</td>\n",
       "      <td>647.001780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Coast        East    Far West       North  \\\n",
       "Hour_Ending                                                            \n",
       "2005-01-01 07:00:00  7507.810731  850.313988  998.465500  741.963778   \n",
       "2005-01-01 08:00:00  7257.549136  818.143264  987.532852  711.996586   \n",
       "2005-01-01 09:00:00  6973.987199  783.396624  978.575662  687.881144   \n",
       "2005-01-01 10:00:00  6794.489296  760.639635  959.486947  673.419383   \n",
       "2005-01-01 11:00:00  6685.186677  743.371691  955.268590  664.792118   \n",
       "\n",
       "                     North Central        South  South Central        West  \n",
       "Hour_Ending                                                                 \n",
       "2005-01-01 07:00:00    7312.259820  1883.979457    3728.400885  692.294503  \n",
       "2005-01-01 08:00:00    7012.466197  1798.234153    3562.614723  675.176013  \n",
       "2005-01-01 09:00:00    6712.807281  1712.758395    3373.868049  659.650453  \n",
       "2005-01-01 10:00:00    6502.378446  1651.210254    3239.047417  648.512894  \n",
       "2005-01-01 11:00:00    6440.559639  1627.872766    3176.859720  647.001780  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ercot_hourly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d6ec186a",
   "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>city</th>\n",
       "      <th>tempC</th>\n",
       "      <th>tempF</th>\n",
       "      <th>windspeedMiles</th>\n",
       "      <th>windspeedKmph</th>\n",
       "      <th>winddirDegree</th>\n",
       "      <th>winddir16Point</th>\n",
       "      <th>weatherCode</th>\n",
       "      <th>weatherDesc</th>\n",
       "      <th>precipMM</th>\n",
       "      <th>precipInches</th>\n",
       "      <th>humidity</th>\n",
       "      <th>visibility</th>\n",
       "      <th>visibilityMiles</th>\n",
       "      <th>pressure</th>\n",
       "      <th>pressureInches</th>\n",
       "      <th>cloudcover</th>\n",
       "      <th>HeatIndexC</th>\n",
       "      <th>HeatIndexF</th>\n",
       "      <th>DewPointC</th>\n",
       "      <th>DewPointF</th>\n",
       "      <th>WindChillC</th>\n",
       "      <th>WindChillF</th>\n",
       "      <th>WindGustMiles</th>\n",
       "      <th>WindGustKmph</th>\n",
       "      <th>FeelsLikeC</th>\n",
       "      <th>FeelsLikeF</th>\n",
       "      <th>uvIndex</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-07-01</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>21</td>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>173</td>\n",
       "      <td>S</td>\n",
       "      <td>113</td>\n",
       "      <td>Clear</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1018</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>76</td>\n",
       "      <td>14</td>\n",
       "      <td>57</td>\n",
       "      <td>21</td>\n",
       "      <td>70</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-01</th>\n",
       "      <td>Austin</td>\n",
       "      <td>25</td>\n",
       "      <td>77</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>103</td>\n",
       "      <td>ESE</td>\n",
       "      <td>176</td>\n",
       "      <td>Patchy rain possible</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1018</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>26</td>\n",
       "      <td>80</td>\n",
       "      <td>17</td>\n",
       "      <td>63</td>\n",
       "      <td>25</td>\n",
       "      <td>77</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>South Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-01</th>\n",
       "      <td>Brownsville</td>\n",
       "      <td>26</td>\n",
       "      <td>79</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>150</td>\n",
       "      <td>SSE</td>\n",
       "      <td>113</td>\n",
       "      <td>Clear</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1017</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>84</td>\n",
       "      <td>23</td>\n",
       "      <td>74</td>\n",
       "      <td>26</td>\n",
       "      <td>79</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>29</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-01</th>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>26</td>\n",
       "      <td>78</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>146</td>\n",
       "      <td>SE</td>\n",
       "      <td>113</td>\n",
       "      <td>Clear</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1017</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>83</td>\n",
       "      <td>23</td>\n",
       "      <td>73</td>\n",
       "      <td>26</td>\n",
       "      <td>78</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>28</td>\n",
       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-01</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>25</td>\n",
       "      <td>76</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>149</td>\n",
       "      <td>SSE</td>\n",
       "      <td>113</td>\n",
       "      <td>Clear</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1018</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>78</td>\n",
       "      <td>12</td>\n",
       "      <td>54</td>\n",
       "      <td>25</td>\n",
       "      <td>76</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>North Central</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      city  tempC  tempF  windspeedMiles  windspeedKmph  \\\n",
       "date                                                                      \n",
       "2008-07-01         Abilene     21     70               4              7   \n",
       "2008-07-01          Austin     25     77               6              9   \n",
       "2008-07-01     Brownsville     26     79               5              8   \n",
       "2008-07-01  Corpus Christi     26     78               8             12   \n",
       "2008-07-01          Dallas     25     76               6              9   \n",
       "\n",
       "            winddirDegree winddir16Point  weatherCode           weatherDesc  \\\n",
       "date                                                                          \n",
       "2008-07-01            173              S          113                 Clear   \n",
       "2008-07-01            103            ESE          176  Patchy rain possible   \n",
       "2008-07-01            150            SSE          113                 Clear   \n",
       "2008-07-01            146             SE          113                 Clear   \n",
       "2008-07-01            149            SSE          113                 Clear   \n",
       "\n",
       "            precipMM  precipInches  humidity  visibility  visibilityMiles  \\\n",
       "date                                                                        \n",
       "2008-07-01       0.0           0.0        63          10                6   \n",
       "2008-07-01       0.6           0.0        61           9                5   \n",
       "2008-07-01       0.0           0.0        86          10                6   \n",
       "2008-07-01       0.0           0.0        83          10                6   \n",
       "2008-07-01       0.0           0.0        46          10                6   \n",
       "\n",
       "            pressure  pressureInches  cloudcover  HeatIndexC  HeatIndexF  \\\n",
       "date                                                                       \n",
       "2008-07-01      1018              31           6          24          76   \n",
       "2008-07-01      1018              31          17          26          80   \n",
       "2008-07-01      1017              31           8          29          84   \n",
       "2008-07-01      1017              30           7          28          83   \n",
       "2008-07-01      1018              31           4          25          78   \n",
       "\n",
       "            DewPointC  DewPointF  WindChillC  WindChillF  WindGustMiles  \\\n",
       "date                                                                      \n",
       "2008-07-01         14         57          21          70              9   \n",
       "2008-07-01         17         63          25          77             11   \n",
       "2008-07-01         23         74          26          79              9   \n",
       "2008-07-01         23         73          26          78             13   \n",
       "2008-07-01         12         54          25          76             11   \n",
       "\n",
       "            WindGustKmph  FeelsLikeC  FeelsLikeF  uvIndex         region  \n",
       "date                                                                      \n",
       "2008-07-01            15          21          70        1           West  \n",
       "2008-07-01            18          26          80        1  South Central  \n",
       "2008-07-01            14          29          84        1          South  \n",
       "2008-07-01            21          28          83        1          South  \n",
       "2008-07-01            18          25          78        1  North Central  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_history.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffd22215",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "5e70d75f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-64-935c01857699>:21: FutureWarning: weekofyear and week have been deprecated, please use DatetimeIndex.isocalendar().week instead, which returns a Series.  To exactly reproduce the behavior of week and weekofyear and return an Index, you may call pd.Int64Index(idx.isocalendar().week)\n",
      "  region_data['week'] = region_data.index.weekofyear\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas.tseries.holiday import USFederalHolidayCalendar as calendar\n",
    "\n",
    "region = 'Coast'\n",
    "region_data = ercot_hourly[[region]].copy()\n",
    "\n",
    "# coast hourly load \n",
    "dr = pd.date_range(start='2008-01-01', end='2021-01-12')\n",
    "cal = calendar()\n",
    "holidays = cal.holidays(start=dr.min(), end=dr.max())\n",
    "\n",
    "region_data['Holiday'] = region_data.index.isin(holidays)\n",
    "region_data['Holiday'] = region_data['Holiday'].astype(int)\n",
    "\n",
    "\n",
    "# adapt dataset for the model\n",
    "region_data['hour'] = region_data.index.hour\n",
    "region_data['month'] = region_data.index.month\n",
    "region_data['year'] = region_data.index.year\n",
    "region_data['dayofyear'] = region_data.index.dayofyear\n",
    "region_data['week'] = region_data.index.weekofyear\n",
    "# region_data['week_day'] = region_data.index.weekday\n",
    "# region_data['days_in_month'] = region_data.index.days_in_month\n",
    "\n",
    "# add weather features\n",
    "region_data['DewPointC'] = add_weatherFeature(region,'DewPointC', weather_history)\n",
    "region_data['uvindex'] = add_weatherFeature(region,'winddirDegree', weather_history)\n",
    "\n",
    "region_data['last_hour'] = region_data[region].shift()\n",
    "region_data['last_hour_diff'] = region_data[region].shift().diff()\n",
    "# region_data['last_2_hours'] = region_data[region].shift(2)\n",
    "region_data['last_2_hours_diff'] = region_data[region].shift(2).diff()\n",
    "# region_data['last_3_hours_diff'] = region_data[region].shift(3).diff()\n",
    "# region_data['last_4_hours_diff'] = region_data[region].shift(4).diff()\n",
    "# region_data['last_5_hours_diff'] = region_data[region].shift(5).diff()\n",
    "# region_data['last_6_hours_diff'] = region_data[region].shift(6).diff()\n",
    "# region_data['last_7_hours_diff'] = region_data[region].shift(7).diff()\n",
    "# region_data['last_8_hours_diff'] = region_data[region].shift(8).diff()\n",
    "# region_data['last_9_hours_diff'] = region_data[region].shift(9).diff()\n",
    "# region_data['last_10_hours_diff'] = region_data[region].shift(10).diff()\n",
    "# region_data['last_11_hours_diff'] = region_data[region].shift(11).diff()\n",
    "# region_data['last_12_hours_diff'] = region_data[region].shift(12).diff()\n",
    "# region_data['last_13_hours_diff'] = region_data[region].shift(13).diff()\n",
    "# region_data['last_14_hours_diff'] = region_data[region].shift(14).diff()\n",
    "# region_data['last_15_hours_diff'] = region_data[region].shift(15).diff()\n",
    "# region_data['last_12_hours_diff'] = region_data[region].shift().diff(16)\n",
    "# region_data['last_6_hours_diff'] = region_data[region].shift().diff(6)\n",
    "# region_data['last_day_diff'] = region_data[region].shift(2).diff()\n",
    "region_data['roll_mean_3'] = region_data[region].shift(23).rolling(2).std()\n",
    "region_data['last_day_diff'] = region_data[region].shift(23).diff()\n",
    "# region_data['last_3_days_diff'] = region_data[region].shift().diff(24*3)\n",
    "region_data['last_week_diff'] = region_data[region].shift(24*7).diff()\n",
    "# region_data['last_4month_diff'] = region_data[region].shift().diff(24*30*4)\n",
    "# region_data['roll_std12_t12'] = region_data[region].transform(lambda x: x.shift(24).rolling(3).std())\n",
    "\n",
    "# make features\n",
    "# region_data['last_hour'] = region_data[region].shift()\n",
    "# region_data['last_hour_diff'] = region_data[region].shift().diff()\n",
    "# region_data['roll_mean_3'] = region_data[region].shift().rolling(2).std()\n",
    "# region_data['last_4_hours_diff'] = region_data[region].shift(4).diff()\n",
    "# region_data['last_12_hours_diff'] = region_data[region].shift(12).diff()\n",
    "# region_data['last_6_hours_diff'] = region_data[region].shift(6).diff()\n",
    "# region_data['last_day_diff'] = region_data[region].shift(24).diff()\n",
    "# region_data['last_2_days_diff'] = region_data[region].shift(24*2).diff()\n",
    "# region_data['last_3_days_diff'] = region_data[region].shift(24*3).diff()\n",
    "# region_data['last_week_diff'] = region_data[region].shift(24*7).diff()\n",
    "# region_data['last_6month_diff'] = region_data[region].shift(24*30*6).diff()\n",
    "# region_data['last_4month_diff'] = region_data[region].shift(24*30*4).diff()\n",
    "# #lags\n",
    "# region_data['roll_std12_t1'] = region_data[region].transform(lambda x: x.shift().rolling(6).std())\n",
    "# region_data['roll_std12_t170'] = region_data[region].transform(lambda x: x.shift().rolling(170).std())\n",
    "# region_data['lag_170'] = region_data[region].shift().diff(170)\n",
    "\n",
    "# region_data['lag_74'] = region_data[region].shift().diff(74)\n",
    "# region_data['lag_98'] = region_data[region].shift().diff(98)\n",
    "# region_data['lag_122'] = region_data[region].shift().diff(122)\n",
    "# region_data['lag_146'] = region_data[region].shift().diff(146)\n",
    "# # region_data['last_day'] = region_data[region].shift(24)\n",
    "# # region_data['last_2_days'] = region_data[region].shift(24*2)\n",
    "# # region_data['last_3_days'] = region_data[region].shift(24*3)\n",
    "# # region_data['last_week'] = region_data[region].shift(24*7)\n",
    "# # region_data['roll_std12_t12'] = region_data[region].transform(lambda x: x.shift(12).rolling(12).std())\n",
    "# # region_data['roll_std12_t24'] = region_data[region].transform(lambda x: x.shift(24).rolling(12).std())\n",
    "# # region_data['roll_std24_t1'] = region_data[region].transform(lambda x: x.shift().rolling(24).std())\n",
    "# # region_data['roll_std24_t12'] = region_data[region].transform(lambda x: x.shift(12).rolling(24).std())\n",
    "# # region_data['roll_std24_t24'] = region_data[region].transform(lambda x: x.shift(24).rolling(24).std())\n",
    "# # region_data['roll_std12_t48'] = region_data[region].transform(lambda x: x.shift(48).rolling(12).std())\n",
    "# # region_data['roll_std24_t48'] = region_data[region].transform(lambda x: x.shift(48).rolling(24).std())\n",
    "\n",
    "region_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4fdb3f2",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41632c52",
   "metadata": {},
   "source": [
    "### TESTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "879fe63d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = region_data.loc['2008':'2020'].reset_index(drop=True)\n",
    "valid = region_data.loc['2020'].reset_index(drop=True)\n",
    "test = region_data.loc['2021':].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "575172fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_lgbm(train,valid,n_est=2200, lr=0.009):\n",
    "    \n",
    "    params = {\n",
    "    'boosting_type':'gbdt',\n",
    "#     'objective':'rmse',\n",
    "    'num_leaves':18,\n",
    "#     'max_depth':1,\n",
    "    'learning_rate':0.08,\n",
    "    'n_estimators':1700,\n",
    "    'force_col_wise':True,\n",
    "#     'min_child_samples':15,\n",
    "#     'min_child_weight':0.01,\n",
    "#     bagging_fraction:0.8,\n",
    "#     feature_fraction:0.9,\n",
    "#     colsample_bytree:0.7,\n",
    "#     reg_alpha:0.0,\n",
    "#     reg_lambda:0.0,\n",
    "    'random_state':42,\n",
    "    'silent':True\n",
    "    }\n",
    "    \n",
    "    drop = [region,'month','week']\n",
    "    X_test, y_test = test.drop(drop, axis=1), test[region]\n",
    "    \n",
    "    predictions=[]\n",
    "    mean_rmse = 0\n",
    "    for week in range(5,25):\n",
    "        # rolling - windows size = week\n",
    "        train_frame = train[((train['year'] == 2020) & (train['week'] < week)) | ((train['year'] < 2020))]\n",
    "        valid_frame = valid[valid['week'] == week]\n",
    "        \n",
    "        # train , valid\n",
    "        X_train, y_train = train_frame.drop(drop, axis=1), train_frame[region]\n",
    "        X_valid, y_valid = valid_frame.drop(drop, axis=1), valid_frame[region]\n",
    "#         eval_set_R = [(X_train, y_train), (X_test, y_test)]\n",
    "        \n",
    "        # train\n",
    "        model = LGBMRegressor(**params, verbose=0)\n",
    "        model.fit(X_train, np.log1p(y_train),\n",
    "#                   eval_metric='rmse',\n",
    "#                   eval_set = eval_set_R,\n",
    "#                   early_stopping_rounds=15,\n",
    "                  verbose=20,)\n",
    "\n",
    "        # validation\n",
    "        pred = np.expm1(model.predict(X_valid))\n",
    "        predictions.append(pred)\n",
    "        \n",
    "        # RMSE\n",
    "        error = round(RMSE(y_valid, pred, squared=False))\n",
    "        print(f'Week {week}: RMSE: {error}')\n",
    "        \n",
    "        # Mean RMSE\n",
    "        mean_rmse += error\n",
    "        \n",
    "    print(f'Mean Error: {round(mean_rmse/20)}')\n",
    "    return(predictions, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4b3462fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Week 5: RMSE: 73\n",
      "Week 6: RMSE: 81\n",
      "Week 7: RMSE: 77\n",
      "Week 8: RMSE: 64\n",
      "Week 9: RMSE: 71\n",
      "Week 10: RMSE: 81\n",
      "Week 11: RMSE: 78\n",
      "Week 12: RMSE: 92\n",
      "Week 13: RMSE: 110\n",
      "Week 14: RMSE: 79\n",
      "Week 15: RMSE: 114\n",
      "Week 16: RMSE: 92\n",
      "Week 17: RMSE: 122\n",
      "Week 18: RMSE: 125\n",
      "Week 19: RMSE: 95\n",
      "Week 20: RMSE: 124\n",
      "Week 21: RMSE: 95\n",
      "Week 22: RMSE: 171\n",
      "Week 23: RMSE: 117\n",
      "Week 24: RMSE: 124\n",
      "Mean Error: 99\n"
     ]
    }
   ],
   "source": [
    "pred, model = train_lgbm(train, valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d1567751",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature importance'}, xlabel='Feature importance', ylabel='Features'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "lgb.plot_importance(model, figsize=(10,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ff0d7895",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Week 5: RMSE: 69\n",
      "Week 6: RMSE: 75\n",
      "Week 7: RMSE: 78\n",
      "Week 8: RMSE: 66\n",
      "Week 9: RMSE: 83\n",
      "Week 10: RMSE: 82\n",
      "Week 11: RMSE: 80\n",
      "Week 12: RMSE: 88\n",
      "Week 13: RMSE: 114\n",
      "Week 14: RMSE: 82\n",
      "Week 15: RMSE: 112\n",
      "Week 16: RMSE: 86\n",
      "Week 17: RMSE: 115\n",
      "Week 18: RMSE: 118\n",
      "Week 19: RMSE: 98\n",
      "Week 20: RMSE: 124\n",
      "Week 21: RMSE: 94\n",
      "Week 22: RMSE: 172\n",
      "Week 23: RMSE: 112\n",
      "Week 24: RMSE: 122\n",
      "Mean Error: 98\n"
     ]
    }
   ],
   "source": [
    "pred, model = train_lgbm(train, valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c161ad2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature importance'}, xlabel='Feature importance', ylabel='Features'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgb.plot_importance(model, figsize=(10,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "39621aa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor(force_col_wise=True, learning_rate=0.08, n_estimators=1700,\n",
       "              num_leaves=18, random_state=42, verbose=0)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train.drop([region,'week','month'],axis=1), np.log1p(train[region]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6f83eac7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75.3863120980259\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_test, y_test= test.drop([region,'month','week'], axis=1), test[[region]].copy()\n",
    "pred = np.expm1(model.predict(x_test.iloc[0:168]))\n",
    "print(RMSE(y_test.iloc[0:168], pred,squared=False))\n",
    "\n",
    "forecasted = test[[region]].copy()\n",
    "forecasted['predict'] = np.expm1(model.predict(x_test))\n",
    "forecasted['resid'] = forecasted[region] - forecasted['predict']\n",
    "forecasted.iloc[:24*7].plot(figsize=(15,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d154603",
   "metadata": {},
   "source": [
    "### LightGBM Week Forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "63f20a29",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightgbm import LGBMRegressor\n",
    "from pandas.tseries.holiday import USFederalHolidayCalendar as calendar\n",
    "\n",
    "\n",
    "class gbm_forecast():\n",
    "    '''\n",
    "    Forecast the next 168 values, for a week forecast.\n",
    "    '''\n",
    "    \n",
    "    def __init__(self, data, region, weather_history, weather_forecast):\n",
    "        self.region = region\n",
    "        self.weather = weather_history.append(weather_forecast).copy()\n",
    "        self.region_data = data[[region,'Hour_Ending']].copy()\n",
    "        self.weather_forecast = weather_forecast.copy()\n",
    "\n",
    "        # preprocess weather and region data\n",
    "        self.preprocess_data()\n",
    "        self.build_features()\n",
    "        \n",
    "        # train and forecast\n",
    "        self.train()\n",
    "        self.forecast()\n",
    "        \n",
    "    def add_weatherFeature(self, feature):\n",
    "        feature_index = self.weather[self.weather['region'] == self.region].index\n",
    "        return(self.weather[self.weather['region'] == self.region].groupby(by=feature_index).mean().resample('1H').bfill()[feature])\n",
    "    \n",
    "    def build_features(self):\n",
    "        \n",
    "        self.region_data['hour'] = self.region_data.index.hour\n",
    "        self.region_data['year'] = self.region_data.index.year\n",
    "        self.region_data['dayofyear'] = self.region_data.index.dayofyear\n",
    "        self.region_data['DewPointC'] = self.add_weatherFeature('DewPointC')\n",
    "        self.region_data['uvIndex'] = self.add_weatherFeature('uvIndex')\n",
    "        self.region_data['last_hour'] = self.region_data[self.region].shift()\n",
    "        self.region_data['last_hour_diff'] = self.region_data[self.region].shift().diff()\n",
    "        self.region_data['last_2_hours_diff'] = self.region_data[self.region].shift(2).diff()\n",
    "#         self.region_data['last_week_diff'] = self.region_data[self.region].shift(24*7).diff()\n",
    "        self.region_data['roll_mean_3'] = self.region_data[self.region].shift(24).rolling(2).std()\n",
    "\n",
    "#         self.region_data['hour'] = self.region_data.index.hour\n",
    "#         self.region_data['year'] = self.region_data.index.year\n",
    "#         self.region_data['dayofyear'] = self.region_data.index.dayofyear\n",
    "#         self.region_data['tempC'] = self.add_weatherFeature('tempC')\n",
    "#         self.region_data['last_hour_std'] = self.region_data[self.region].rolling(2).std()\n",
    "#         self.region_data['last_hour_diff'] = self.region_data[self.region].shift().diff()\n",
    "#         self.region_data['last_2_hours_diff'] = self.region_data[self.region].shift(2).diff()\n",
    "#         self.region_data['last_2_hours_std'] = self.region_data[self.region].rolling(3).std()\n",
    "#         self.region_data['roll_mean_3'] = self.region_data[self.region].shift(24).rolling(2).std()\n",
    "        \n",
    "        self.region_data.dropna(inplace=True)\n",
    "        \n",
    "    def preprocess_data(self, data=None):\n",
    "        # PREPROCESS WEATHER\n",
    "        \n",
    "        # transform time into hours:minutes\n",
    "        time = {'time':{0:'00:00',300:'03:00',600:'06:00',900:'09:00',1200:'12:00',\n",
    "                        1500:'15:00',1800:'18:00',2100:'21:00'}}\n",
    "        self.weather.replace(time, inplace=True)\n",
    "\n",
    "        # join date + time\n",
    "        self.weather['date'] = self.weather.date + \" \" + self.weather.time.map(str)\n",
    "        self.weather.drop(columns={'time'}, inplace=True)\n",
    "\n",
    "        # make timeindex\n",
    "        self.weather.date = pd.DatetimeIndex(self.weather.date)\n",
    "        self.weather.set_index('date', inplace=True)\n",
    "\n",
    "        # transform city to region\n",
    "        city_region = {'Wichita Falls':'North',\n",
    "                       'Tyler':'East',\n",
    "                       'Corpus Christi':'South',\n",
    "                       'Brownsville':'South',\n",
    "                       'Dallas':'North Central',\n",
    "                       'Austin':'South Central',\n",
    "                       'Midland':'Far West',\n",
    "                       'San Antonio':'South Central',\n",
    "                       'Houston':'Coast',\n",
    "                       'Abilene':'West'}\n",
    "\n",
    "        self.weather['region'] = self.weather.city.replace(city_region)\n",
    "\n",
    "        \n",
    "        # PREPROCESS POWER DEMAND ERCOT HOURLY      \n",
    "        \n",
    "        # Convert CST & CDT to UTC\n",
    "        self.region_data['Hour_Ending']= pd.to_datetime(self.region_data['Hour_Ending'],utc= True)\n",
    "        # Datetimeindex\n",
    "        self.region_data.set_index(\"Hour_Ending\", inplace=True)\n",
    "        self.region_data = self.region_data.tz_convert(tz='US/Central').tz_localize(tz=None)       \n",
    "        \n",
    "        # add hollidays\n",
    "        dr = pd.date_range(start='2008-01-01', end='2021-06-20')\n",
    "        cal = calendar()\n",
    "        self.holidays = cal.holidays(start=dr.min(), end=dr.max())\n",
    "\n",
    "        self.region_data['Holiday'] = self.region_data.index.isin(self.holidays)\n",
    "        self.region_data['Holiday'] = self.region_data['Holiday'].astype(int)\n",
    "        \n",
    "    def train(self):\n",
    "        \n",
    "        train = self.region_data.reset_index(drop=True)\n",
    "        \n",
    "        params = {\n",
    "            'boosting_type':'gbdt',\n",
    "            'num_leaves':18,\n",
    "        #     'max_depth':1,\n",
    "            'learning_rate':0.08,\n",
    "            'n_estimators':1500,\n",
    "            'force_col_wise':True,\n",
    "            'random_state':42,\n",
    "            'silent':True\n",
    "            }\n",
    "        \n",
    "        self.model = LGBMRegressor(**params)\n",
    "        self.model.fit(train.drop([self.region],axis=1), np.log1p(train[self.region]))\n",
    "        self.train_data = train\n",
    "    \n",
    "    def forecast(self):\n",
    "        \n",
    "        forecasted = []\n",
    "        # week forecast\n",
    "        for idx in range(24*7):\n",
    "            region_data = self.region_data.iloc[[-1]].copy()\n",
    "\n",
    "            point_value = region_data.drop([self.region],axis=1)\n",
    "\n",
    "            forecasted.append(np.expm1(self.model.predict(point_value)[0]))\n",
    "\n",
    "            if region_data.index.hour == 0:\n",
    "                region_data['hour'] = 1\n",
    "                region_data['dayofyear'] = region_data.index.dayofyear +1\n",
    "\n",
    "            else:\n",
    "                region_data['hour'] = region_data.index.hour + 1\n",
    "\n",
    "            # make next dataframe\n",
    "            region_data['last_hour'] = self.region_data[self.region].iloc[-1]\n",
    "            region_data['last_hour_diff'] = self.region_data[self.region].diff().iloc[-1]\n",
    "            region_data['last_2_hours_diff'] = self.region_data[self.region].shift(1).diff().iloc[-1]\n",
    "#             region_data['last_week_diff'] = self.region_data[self.region].shift(24*7).diff().iloc[-1]\n",
    "            region_data['roll_mean_3'] = self.region_data[self.region].shift(23).rolling(2).std()[-1]\n",
    "            region_data.index += pd.TimedeltaIndex([1], unit='h')\n",
    "            region_data['DewPointC'] = self.add_weatherFeature('DewPointC')\n",
    "            region_data['uvIndex'] = self.add_weatherFeature('uvIndex')\n",
    "            region_data['Holiday'] = region_data.index.isin(self.holidays)\n",
    "            region_data['Holiday'] = region_data['Holiday'].astype(int) \n",
    "            region_data[self.region] = np.expm1(self.model.predict(region_data.drop([self.region],axis=1)))\n",
    "\n",
    "#             region_data['last_hour_std'] = self.region_data[self.region].rolling(2).std().iloc[-1]\n",
    "#             region_data['last_hour_diff'] = self.region_data[self.region].diff().iloc[-1]\n",
    "#             region_data['last_2_hours_diff'] = self.region_data[self.region].shift(1).diff().iloc[-1]\n",
    "#             region_data['last_2_hours_std'] = self.region_data[self.region].rolling(2).std().iloc[-1]\n",
    "#             region_data['roll_mean_3'] = self.region_data[self.region].shift(24).rolling(2).std()[-1]\n",
    "#             region_data.index += pd.TimedeltaIndex([1], unit='h')\n",
    "#             region_data['tempC'] = self.add_weatherFeature('tempC')\n",
    "#             region_data['Holiday'] = region_data.index.isin(self.holidays)\n",
    "#             region_data['Holiday'] = region_data['Holiday'].astype(int) \n",
    "#             region_data[self.region] = np.expm1(self.model.predict(region_data.drop([self.region],axis=1)))\n",
    "            \n",
    "            # append new data to train \n",
    "            self.region_data = self.region_data.append(region_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "23101979",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "ercot_hourly = pd.read_csv(path+'/ercot_hourly_load.csv')\n",
    "ercot_hourly.fillna(method='backfill',inplace=True)\n",
    "\n",
    "# forecast\n",
    "data = ercot_hourly[(ercot_hourly['Hour_Ending'] < '2021') & (ercot_hourly['Hour_Ending'] > '2014')].copy()\n",
    "coast = gbm_forecast(data,'Coast', weather_history, weather_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "0aa72965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Hour_Ending'>"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preprocess_data(ercot_hourly, weather_history.copy())\n",
    "gbm_forecast = ercot_hourly.loc[:'2021'].tz_localize(tz='utc').tz_convert(tz='US/Central')[['Coast']].copy()\n",
    "gbm_forecast = gbm_forecast.tz_localize(tz=None)\n",
    "gbm_forecast['GBM Coast'] = coast.region_data.tail(24*7)['Coast']\n",
    "gbm_forecast.dropna(inplace=True)\n",
    "gbm_forecast.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "58fbcfd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "752.29424690362\n"
     ]
    }
   ],
   "source": [
    "gbm_rmse = RMSE(gbm_forecast['Coast'].values,gbm_forecast['GBM Coast'].values, squared=False)\n",
    "print(gbm_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "befe2b30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature importance'}, xlabel='Feature importance', ylabel='Features'>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "lgb.plot_importance(coast.model, figsize=(10,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f4773d",
   "metadata": {},
   "source": [
    "## LSTM Week Forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "fe2d9541",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import functools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "class forecast_LSTM():\n",
    "    def __init__(self, model,data,region):\n",
    "        self.data = data.copy()\n",
    "        self.model = model\n",
    "        self.region = region\n",
    "        \n",
    "        self.preprocess()\n",
    "        self.forecast()\n",
    "        \n",
    "    def preprocess(self):\n",
    "        \n",
    "        # Convert CST & CDT to UTC\n",
    "        self.data['Hour_Ending']= pd.to_datetime(self.data['Hour_Ending'],utc= True)\n",
    "        # Datetimeindex\n",
    "        self.data.set_index(\"Hour_Ending\", inplace=True)\n",
    "        self.data = self.data.tz_convert(tz='US/Central')\n",
    "        self.data = self.data.tz_localize(tz=None)\n",
    "        \n",
    "    def forecast(self):\n",
    "        window = 24*7\n",
    "        train_test = self.data.tail(window)[self.region].values\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(self.data[self.region].values.reshape(-1,1))\n",
    "        train_t_scaled = scaler.fit_transform(train_test.reshape(-1,1))\n",
    "        forcast = []\n",
    "        for idx in range(0,24*7,24):\n",
    "            pred = self.model_forecast(self.model, train_t_scaled[idx:window + idx][...,np.newaxis], 1).reshape(-1,1)[:24]\n",
    "            forcast = np.append(forcast,pred)\n",
    "            train_t_scaled = np.append(train_t_scaled,pred)\n",
    "            \n",
    "        self.forecast = scaler.inverse_transform(forcast)\n",
    "        \n",
    "    # transforma os dados para previsões\n",
    "    def model_forecast(self,model, series, window_size):\n",
    "        ds = tf.data.Dataset.from_tensor_slices(series)\n",
    "        ds = ds.window(window_size, shift=1, drop_remainder=True)\n",
    "        ds = ds.flat_map(lambda w: w.batch(window_size))\n",
    "        ds = ds.batch(32).prefetch(1)\n",
    "        forecast = model.predict(ds)\n",
    "        return(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "1f25c8bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load LSTM Model\n",
    "checkpoint_path = 'lstm_models_checkpoints'\n",
    "model = tf.keras.models.load_model(checkpoint_path+'/best_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "e0d5e23a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ercot_hourly = pd.read_csv(path+'/ercot_hourly_load.csv')\n",
    "ercot_hourly.fillna(method='backfill',inplace=True)\n",
    "\n",
    "data = ercot_hourly[ercot_hourly['Hour_Ending'] < '2021'].copy()\n",
    "coast_lstm = forecast_LSTM(model,data,'Coast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "1cf957dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1239460fd0>]"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(coast_lstm.forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "6e5fc366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Hour_Ending'>"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# preprocess ercot hourly\n",
    "preprocess_data(ercot_hourly, weather_history.copy())\n",
    "\n",
    "# lstm_forecast x test\n",
    "lstm_forecast = ercot_hourly.tz_localize(tz='utc').tz_convert(tz='US/Central')[['Coast']].copy()\n",
    "lstm_forecast = lstm_forecast.tz_localize(tz=None)\n",
    "lstm_forecast = lstm_forecast.loc['2021'].head(24*7)\n",
    "lstm_forecast['pred'] = coast_lstm.forecast\n",
    "lstm_forecast.dropna(inplace=True)\n",
    "lstm_forecast.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "45cd5c75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LSTM RMSE: 678.8734175055857\n"
     ]
    }
   ],
   "source": [
    "lstm_rmse = RMSE(forecast['Coast'].values,forecast['pred'].values, squared=False)\n",
    "print(f'LSTM RMSE: {lstm_rmse}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e409ff94",
   "metadata": {},
   "source": [
    "## LSTM with LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "06557860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Hour_Ending'>"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbm_forecast['LSTM Coast'] = lstm_forecast['pred'].copy()\n",
    "gbm_forecast['LSTM + GBM'] = (gbm_forecast['LSTM Coast'] + gbm_forecast['GBM Coast'])/2\n",
    "gbm_forecast[['Coast','LSTM + GBM']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "3601d959",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LSTM RMSE: 571.9156075172118\n"
     ]
    }
   ],
   "source": [
    "lstm_gbm_rmse = RMSE(gbm_forecast['Coast'].values,gbm_forecast['LSTM + GBM'].values, squared=False)\n",
    "print(f'LSTM RMSE: {lstm_gbm_rmse}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6addb054",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "The best solution was the two models LSTM and LightGBM working together, as expected."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "568ce60a",
   "metadata": {},
   "source": [
    "### This is the final of the project.\n",
    "For the competition, we trained for each region the two models and joined their forecast.\n",
    "\n",
    "**PS: For some Regions we had to build some other features, also use other weather's features. As we mentioned in the Data Understanding notebook.**"
   ]
  }
 ],
 "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}