LoadModel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the model from save state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reconstructing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "path = os.getcwd()\n",
    "models_path = path + \"\\\\Models\"\n",
    "\n",
    "reconstructed_model = keras.models.load_model(models_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the reconstructed model on new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>con no1 mwh/h cet min15 a</th>\n",
       "      <th>INSTANCES(tt no1 con ec00 °c cet min15 f)</th>\n",
       "      <th>INSTANCES(pro no1 wnd ec00 mwh/h cet min15 f)</th>\n",
       "      <th>INSTANCES(pro no1 spv ec00 mwh/h cet min15 f)</th>\n",
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       "      <th>2020-06-15 23:00:00+02:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19.9</td>\n",
       "      <td>33.172425</td>\n",
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       "    <tr>\n",
       "      <th>2020-06-16 00:00:00+02:00</th>\n",
       "      <td>2549.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-06-16 01:00:00+02:00</th>\n",
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       "      <th>2020-06-16 02:00:00+02:00</th>\n",
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       "      <th>2020-06-16 03:00:00+02:00</th>\n",
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       "      <th>2020-09-15 19:00:00+02:00</th>\n",
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       "      <th>2020-09-15 20:00:00+02:00</th>\n",
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       "<p>2209 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "                           con no1 mwh/h cet min15 a  \\\n",
       "2020-06-15 23:00:00+02:00                        NaN   \n",
       "2020-06-16 00:00:00+02:00                     2549.0   \n",
       "2020-06-16 01:00:00+02:00                     2356.0   \n",
       "2020-06-16 02:00:00+02:00                     2227.0   \n",
       "2020-06-16 03:00:00+02:00                     2159.0   \n",
       "...                                              ...   \n",
       "2020-09-15 19:00:00+02:00                     3436.0   \n",
       "2020-09-15 20:00:00+02:00                     3433.0   \n",
       "2020-09-15 21:00:00+02:00                     3265.0   \n",
       "2020-09-15 22:00:00+02:00                     3051.0   \n",
       "2020-09-15 23:00:00+02:00                     2740.0   \n",
       "\n",
       "                           INSTANCES(tt no1 con ec00 °c cet min15 f)  \\\n",
       "2020-06-15 23:00:00+02:00                                       19.9   \n",
       "2020-06-16 00:00:00+02:00                                       19.2   \n",
       "2020-06-16 01:00:00+02:00                                       18.7   \n",
       "2020-06-16 02:00:00+02:00                                       18.1   \n",
       "2020-06-16 03:00:00+02:00                                       17.2   \n",
       "...                                                              ...   \n",
       "2020-09-15 19:00:00+02:00                                       14.6   \n",
       "2020-09-15 20:00:00+02:00                                       13.6   \n",
       "2020-09-15 21:00:00+02:00                                       13.0   \n",
       "2020-09-15 22:00:00+02:00                                       12.8   \n",
       "2020-09-15 23:00:00+02:00                                        NaN   \n",
       "\n",
       "                           INSTANCES(pro no1 wnd ec00 mwh/h cet min15 f)  \\\n",
       "2020-06-15 23:00:00+02:00                                      33.172425   \n",
       "2020-06-16 00:00:00+02:00                                      27.898860   \n",
       "2020-06-16 01:00:00+02:00                                      24.666675   \n",
       "2020-06-16 02:00:00+02:00                                      21.774720   \n",
       "2020-06-16 03:00:00+02:00                                      18.712650   \n",
       "...                                                                  ...   \n",
       "2020-09-15 19:00:00+02:00                                       6.294255   \n",
       "2020-09-15 20:00:00+02:00                                       8.165520   \n",
       "2020-09-15 21:00:00+02:00                                      11.397705   \n",
       "2020-09-15 22:00:00+02:00                                      14.629890   \n",
       "2020-09-15 23:00:00+02:00                                            NaN   \n",
       "\n",
       "                           INSTANCES(pro no1 spv ec00 mwh/h cet min15 f)  \n",
       "2020-06-15 23:00:00+02:00                                       0.000000  \n",
       "2020-06-16 00:00:00+02:00                                       0.000000  \n",
       "2020-06-16 01:00:00+02:00                                       0.000000  \n",
       "2020-06-16 02:00:00+02:00                                       0.000000  \n",
       "2020-06-16 03:00:00+02:00                                       0.000000  \n",
       "...                                                                  ...  \n",
       "2020-09-15 19:00:00+02:00                                       0.283921  \n",
       "2020-09-15 20:00:00+02:00                                       0.000000  \n",
       "2020-09-15 21:00:00+02:00                                       0.000000  \n",
       "2020-09-15 22:00:00+02:00                                       0.000000  \n",
       "2020-09-15 23:00:00+02:00                                            NaN  \n",
       "\n",
       "[2209 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wapi # wattsights egen pakke / den må installers via `pip install wapi-python`\n",
    "import pandas as pd\n",
    "from datetime import datetime \n",
    "from datetime import timedelta\n",
    "\n",
    "# Create a session\n",
    "# Dette forutsetter at dere har lagret volue credentials som miljøvariabler på PC'en deres og med navnet `WS_CLIENT_ID` og `WS_CLIENT_SECRET`\n",
    "user = os.getenv(\"WS_CLIENT_ID\")\n",
    "password = os.getenv(\"WS_CLIENT_SECRET\")\n",
    "session = wapi.Session(client_id=user, client_secret=password)\n",
    "\n",
    "start = '2020-06-16'\n",
    "end = '2020-09-16'\n",
    "periods=pd.date_range(start,end,freq='D')\n",
    "\n",
    "curves = ['con no1 mwh/h cet min15 a',\n",
    "'tt no1 con ec00 °c cet min15 f',\n",
    "'pro no1 wnd ec00 mwh/h cet min15 f',\n",
    "'pro no1 spv ec00 mwh/h cet min15 f',\n",
    "]\n",
    "\n",
    "\n",
    "\n",
    "df_watt = pd.DataFrame()\n",
    "for curve in curves:\n",
    "    print(f\"Working with {curve}\")\n",
    "    curve = session.get_curve(name=curve)\n",
    "    curve_type = curve.curve_type\n",
    "    res = []\n",
    "    counter = 1\n",
    "    if curve_type == 'TIME_SERIES':\n",
    "        ts = curve.get_data(data_from=start, data_to=end, function='SAVERAGE', frequency='H')\n",
    "        watt_temp = ts.to_pandas()\n",
    "        df_watt = pd.merge(df_watt, watt_temp, left_index=True, right_index=True, how='outer')\n",
    "        continue\n",
    "    \n",
    "    for j in range(len(periods)):\n",
    "        if j == len(periods)-1:\n",
    "            continue\n",
    "        if curve_type == 'TAGGED_INSTANCES':\n",
    "            ts_list = curve.search_instances(issue_date_from=periods[j],\n",
    "                                 issue_date_to=periods[j+1],\n",
    "                                 with_data=True, function='SAVERAGE', frequency='H', tags=['Avg'])\n",
    "        else: #curve_type == 'INSTANCES'\n",
    "            ts_list = curve.search_instances(issue_date_from=periods[j],\n",
    "                                 issue_date_to=periods[j+1],\n",
    "                                 with_data=True, function='SAVERAGE', frequency='H')\n",
    "        # check if ts_list is empty and if yes, jump over that timestamp\n",
    "        if not ts_list:\n",
    "            continue\n",
    "        pd_s = ts_list[0].to_pandas()\n",
    "        if pd_s.empty:\n",
    "            continue\n",
    "        pd_s = pd_s.sort_index(ascending=True)\n",
    "        pd_s = pd_s[:24]\n",
    "        res.append(pd_s)\n",
    "        print(f'Done {counter} out of {len(periods)}')\n",
    "        counter += 1\n",
    "\n",
    "    for k in range(len(res)):\n",
    "        if k == 0:\n",
    "            watt = pd.Series(res[k])\n",
    "        else:\n",
    "            watt = pd.concat([watt, res[k]])\n",
    "    watt_temp = watt.rename(curve)\n",
    "    df_watt = pd.merge(df_watt, watt_temp, left_index=True, right_index=True, how='outer')\n",
    "\n",
    "df_watt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cons_actual</th>\n",
       "      <th>temp_forecast</th>\n",
       "      <th>wind_forecast</th>\n",
       "      <th>solar_forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-06-16 00:00:00+02:00</th>\n",
       "      <td>2549.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>27.898860</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-16 01:00:00+02:00</th>\n",
       "      <td>2356.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>24.666675</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-16 02:00:00+02:00</th>\n",
       "      <td>2227.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>21.774720</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-16 03:00:00+02:00</th>\n",
       "      <td>2159.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>18.712650</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-16 04:00:00+02:00</th>\n",
       "      <td>2158.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>13.609200</td>\n",
       "      <td>0.000054</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>2020-09-15 18:00:00+02:00</th>\n",
       "      <td>3423.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>7.144830</td>\n",
       "      <td>3.869835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-15 19:00:00+02:00</th>\n",
       "      <td>3436.0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>6.294255</td>\n",
       "      <td>0.283921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-15 20:00:00+02:00</th>\n",
       "      <td>3433.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>8.165520</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-15 21:00:00+02:00</th>\n",
       "      <td>3265.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.397705</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-15 22:00:00+02:00</th>\n",
       "      <td>3051.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>14.629890</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2207 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           cons_actual  temp_forecast  wind_forecast  \\\n",
       "2020-06-16 00:00:00+02:00       2549.0           19.2      27.898860   \n",
       "2020-06-16 01:00:00+02:00       2356.0           18.7      24.666675   \n",
       "2020-06-16 02:00:00+02:00       2227.0           18.1      21.774720   \n",
       "2020-06-16 03:00:00+02:00       2159.0           17.2      18.712650   \n",
       "2020-06-16 04:00:00+02:00       2158.0           16.9      13.609200   \n",
       "...                                ...            ...            ...   \n",
       "2020-09-15 18:00:00+02:00       3423.0           15.9       7.144830   \n",
       "2020-09-15 19:00:00+02:00       3436.0           14.6       6.294255   \n",
       "2020-09-15 20:00:00+02:00       3433.0           13.6       8.165520   \n",
       "2020-09-15 21:00:00+02:00       3265.0           13.0      11.397705   \n",
       "2020-09-15 22:00:00+02:00       3051.0           12.8      14.629890   \n",
       "\n",
       "                           solar_forecast  \n",
       "2020-06-16 00:00:00+02:00        0.000000  \n",
       "2020-06-16 01:00:00+02:00        0.000000  \n",
       "2020-06-16 02:00:00+02:00        0.000000  \n",
       "2020-06-16 03:00:00+02:00        0.000000  \n",
       "2020-06-16 04:00:00+02:00        0.000054  \n",
       "...                                   ...  \n",
       "2020-09-15 18:00:00+02:00        3.869835  \n",
       "2020-09-15 19:00:00+02:00        0.283921  \n",
       "2020-09-15 20:00:00+02:00        0.000000  \n",
       "2020-09-15 21:00:00+02:00        0.000000  \n",
       "2020-09-15 22:00:00+02:00        0.000000  \n",
       "\n",
       "[2207 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_watt.columns = [\"cons_actual\", \"temp_forecast\", \"wind_forecast\", \"solar_forecast\"]\n",
    "df_watt.dropna(inplace=True)\n",
    "df_watt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cons_actual</th>\n",
       "      <th>temp_forecast</th>\n",
       "      <th>wind_forecast</th>\n",
       "      <th>solar_forecast</th>\n",
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       "      <th>Day cos</th>\n",
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       "      <th>Year sin</th>\n",
       "      <th>Year cos</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>2020-06-16 00:00:00+02:00</th>\n",
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       "      <th>2020-06-16 01:00:00+02:00</th>\n",
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       "    <tr>\n",
       "      <th>2020-06-16 02:00:00+02:00</th>\n",
       "      <td>2227.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-06-16 03:00:00+02:00</th>\n",
       "      <td>2159.0</td>\n",
       "      <td>17.2</td>\n",
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       "    <tr>\n",
       "      <th>2020-06-16 04:00:00+02:00</th>\n",
       "      <td>2158.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>13.609200</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2020-09-15 18:00:00+02:00</th>\n",
       "      <td>3423.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>7.144830</td>\n",
       "      <td>3.869835</td>\n",
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       "      <td>-0.965157</td>\n",
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       "    <tr>\n",
       "      <th>2020-09-15 19:00:00+02:00</th>\n",
       "      <td>3436.0</td>\n",
       "      <td>14.6</td>\n",
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       "    <tr>\n",
       "      <th>2020-09-15 20:00:00+02:00</th>\n",
       "      <td>3433.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>8.165520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.228297e-11</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>0.433884</td>\n",
       "      <td>-0.965531</td>\n",
       "      <td>-0.260288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-15 21:00:00+02:00</th>\n",
       "      <td>3265.0</td>\n",
       "      <td>13.0</td>\n",
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       "      <td>-0.965717</td>\n",
       "      <td>-0.259596</td>\n",
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       "    <tr>\n",
       "      <th>2020-09-15 22:00:00+02:00</th>\n",
       "      <td>3051.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>14.629890</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>5.000000e-01</td>\n",
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       "      <td>-0.965903</td>\n",
       "      <td>-0.258904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2207 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           cons_actual  temp_forecast  wind_forecast  \\\n",
       "2020-06-16 00:00:00+02:00       2549.0           19.2      27.898860   \n",
       "2020-06-16 01:00:00+02:00       2356.0           18.7      24.666675   \n",
       "2020-06-16 02:00:00+02:00       2227.0           18.1      21.774720   \n",
       "2020-06-16 03:00:00+02:00       2159.0           17.2      18.712650   \n",
       "2020-06-16 04:00:00+02:00       2158.0           16.9      13.609200   \n",
       "...                                ...            ...            ...   \n",
       "2020-09-15 18:00:00+02:00       3423.0           15.9       7.144830   \n",
       "2020-09-15 19:00:00+02:00       3436.0           14.6       6.294255   \n",
       "2020-09-15 20:00:00+02:00       3433.0           13.6       8.165520   \n",
       "2020-09-15 21:00:00+02:00       3265.0           13.0      11.397705   \n",
       "2020-09-15 22:00:00+02:00       3051.0           12.8      14.629890   \n",
       "\n",
       "                           solar_forecast       Day sin       Day cos  \\\n",
       "2020-06-16 00:00:00+02:00        0.000000 -5.000000e-01  8.660254e-01   \n",
       "2020-06-16 01:00:00+02:00        0.000000 -2.588190e-01  9.659258e-01   \n",
       "2020-06-16 02:00:00+02:00        0.000000 -2.858239e-12  1.000000e+00   \n",
       "2020-06-16 03:00:00+02:00        0.000000  2.588190e-01  9.659258e-01   \n",
       "2020-06-16 04:00:00+02:00        0.000054  5.000000e-01  8.660254e-01   \n",
       "...                                   ...           ...           ...   \n",
       "2020-09-15 18:00:00+02:00        3.869835 -8.660254e-01 -5.000000e-01   \n",
       "2020-09-15 19:00:00+02:00        0.283921 -9.659258e-01 -2.588190e-01   \n",
       "2020-09-15 20:00:00+02:00        0.000000 -1.000000e+00 -1.228297e-11   \n",
       "2020-09-15 21:00:00+02:00        0.000000 -9.659258e-01  2.588190e-01   \n",
       "2020-09-15 22:00:00+02:00        0.000000 -8.660254e-01  5.000000e-01   \n",
       "\n",
       "                           Week sin  Week cos  Year sin  Year cos  \n",
       "2020-06-16 00:00:00+02:00 -0.955573 -0.294755  0.268960 -0.963151  \n",
       "2020-06-16 01:00:00+02:00 -0.965926 -0.258819  0.268269 -0.963344  \n",
       "2020-06-16 02:00:00+02:00 -0.974928 -0.222521  0.267579 -0.963536  \n",
       "2020-06-16 03:00:00+02:00 -0.982566 -0.185912  0.266888 -0.963728  \n",
       "2020-06-16 04:00:00+02:00 -0.988831 -0.149042  0.266197 -0.963919  \n",
       "...                             ...       ...       ...       ...  \n",
       "2020-09-15 18:00:00+02:00 -0.930874  0.365341 -0.965157 -0.261672  \n",
       "2020-09-15 19:00:00+02:00 -0.916562  0.399892 -0.965344 -0.260980  \n",
       "2020-09-15 20:00:00+02:00 -0.900969  0.433884 -0.965531 -0.260288  \n",
       "2020-09-15 21:00:00+02:00 -0.884115  0.467269 -0.965717 -0.259596  \n",
       "2020-09-15 22:00:00+02:00 -0.866025  0.500000 -0.965903 -0.258904  \n",
       "\n",
       "[2207 rows x 10 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_watt['Seconds'] = df_watt.index.map(pd.Timestamp.timestamp)\n",
    "day = 60*60*24\n",
    "year = 365.2425*day\n",
    "week = day*7\n",
    "\n",
    "df_watt['Day sin'] = np.sin(df_watt['Seconds'] * (2* np.pi / day))\n",
    "df_watt['Day cos'] = np.cos(df_watt['Seconds'] * (2 * np.pi / day))\n",
    "df_watt['Week sin'] = np.sin(df_watt['Seconds'] * (2 * np.pi / week))\n",
    "df_watt['Week cos'] = np.cos(df_watt['Seconds'] * (2 * np.pi / week))\n",
    "df_watt['Year sin'] = np.sin(df_watt['Seconds'] * (2 * np.pi / year))\n",
    "df_watt['Year cos'] = np.cos(df_watt['Seconds'] * (2 * np.pi / year))\n",
    "\n",
    "df_watt=df_watt.drop(['Seconds'], axis=1)\n",
    "\n",
    "df_watt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>2020-06-16 00:00:00+02:00</th>\n",
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       "    <tr>\n",
       "      <th>2020-06-16 02:00:00+02:00</th>\n",
       "      <td>2227.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>21.774720</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.858239e-12</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>0.267579</td>\n",
       "      <td>-0.963536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-16 03:00:00+02:00</th>\n",
       "      <td>2159.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>18.712650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>9.659258e-01</td>\n",
       "      <td>-0.982566</td>\n",
       "      <td>-0.185912</td>\n",
       "      <td>0.266888</td>\n",
       "      <td>-0.963728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-16 04:00:00+02:00</th>\n",
       "      <td>2158.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>13.609200</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>-0.988831</td>\n",
       "      <td>-0.149042</td>\n",
       "      <td>0.266197</td>\n",
       "      <td>-0.963919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-15 18:00:00+02:00</th>\n",
       "      <td>3423.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>7.144830</td>\n",
       "      <td>3.869835</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>-0.930874</td>\n",
       "      <td>0.365341</td>\n",
       "      <td>-0.965157</td>\n",
       "      <td>-0.261672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-15 19:00:00+02:00</th>\n",
       "      <td>3436.0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>6.294255</td>\n",
       "      <td>0.283921</td>\n",
       "      <td>-9.659258e-01</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>-0.916562</td>\n",
       "      <td>0.399892</td>\n",
       "      <td>-0.965344</td>\n",
       "      <td>-0.260980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-15 20:00:00+02:00</th>\n",
       "      <td>3433.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>8.165520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.228297e-11</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>0.433884</td>\n",
       "      <td>-0.965531</td>\n",
       "      <td>-0.260288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-15 21:00:00+02:00</th>\n",
       "      <td>3265.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.397705</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9.659258e-01</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>-0.884115</td>\n",
       "      <td>0.467269</td>\n",
       "      <td>-0.965717</td>\n",
       "      <td>-0.259596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-15 22:00:00+02:00</th>\n",
       "      <td>3051.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>14.629890</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.965903</td>\n",
       "      <td>-0.258904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2207 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           cons_actual  temp_forecast  wind_forecast  \\\n",
       "2020-06-16 00:00:00+02:00       2549.0           19.2      27.898860   \n",
       "2020-06-16 01:00:00+02:00       2356.0           18.7      24.666675   \n",
       "2020-06-16 02:00:00+02:00       2227.0           18.1      21.774720   \n",
       "2020-06-16 03:00:00+02:00       2159.0           17.2      18.712650   \n",
       "2020-06-16 04:00:00+02:00       2158.0           16.9      13.609200   \n",
       "...                                ...            ...            ...   \n",
       "2020-09-15 18:00:00+02:00       3423.0           15.9       7.144830   \n",
       "2020-09-15 19:00:00+02:00       3436.0           14.6       6.294255   \n",
       "2020-09-15 20:00:00+02:00       3433.0           13.6       8.165520   \n",
       "2020-09-15 21:00:00+02:00       3265.0           13.0      11.397705   \n",
       "2020-09-15 22:00:00+02:00       3051.0           12.8      14.629890   \n",
       "\n",
       "                           solar_forecast       Day sin       Day cos  \\\n",
       "2020-06-16 00:00:00+02:00        0.000000 -5.000000e-01  8.660254e-01   \n",
       "2020-06-16 01:00:00+02:00        0.000000 -2.588190e-01  9.659258e-01   \n",
       "2020-06-16 02:00:00+02:00        0.000000 -2.858239e-12  1.000000e+00   \n",
       "2020-06-16 03:00:00+02:00        0.000000  2.588190e-01  9.659258e-01   \n",
       "2020-06-16 04:00:00+02:00        0.000054  5.000000e-01  8.660254e-01   \n",
       "...                                   ...           ...           ...   \n",
       "2020-09-15 18:00:00+02:00        3.869835 -8.660254e-01 -5.000000e-01   \n",
       "2020-09-15 19:00:00+02:00        0.283921 -9.659258e-01 -2.588190e-01   \n",
       "2020-09-15 20:00:00+02:00        0.000000 -1.000000e+00 -1.228297e-11   \n",
       "2020-09-15 21:00:00+02:00        0.000000 -9.659258e-01  2.588190e-01   \n",
       "2020-09-15 22:00:00+02:00        0.000000 -8.660254e-01  5.000000e-01   \n",
       "\n",
       "                           Week sin  Week cos  Year sin  Year cos  \n",
       "2020-06-16 00:00:00+02:00 -0.955573 -0.294755  0.268960 -0.963151  \n",
       "2020-06-16 01:00:00+02:00 -0.965926 -0.258819  0.268269 -0.963344  \n",
       "2020-06-16 02:00:00+02:00 -0.974928 -0.222521  0.267579 -0.963536  \n",
       "2020-06-16 03:00:00+02:00 -0.982566 -0.185912  0.266888 -0.963728  \n",
       "2020-06-16 04:00:00+02:00 -0.988831 -0.149042  0.266197 -0.963919  \n",
       "...                             ...       ...       ...       ...  \n",
       "2020-09-15 18:00:00+02:00 -0.930874  0.365341 -0.965157 -0.261672  \n",
       "2020-09-15 19:00:00+02:00 -0.916562  0.399892 -0.965344 -0.260980  \n",
       "2020-09-15 20:00:00+02:00 -0.900969  0.433884 -0.965531 -0.260288  \n",
       "2020-09-15 21:00:00+02:00 -0.884115  0.467269 -0.965717 -0.259596  \n",
       "2020-09-15 22:00:00+02:00 -0.866025  0.500000 -0.965903 -0.258904  \n",
       "\n",
       "[2207 rows x 10 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = list(df_watt)[0:10]\n",
    "#New dataframe with only training data - 4 columns\n",
    "df_for_training = df_watt[cols].astype(float)\n",
    "df_for_training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.66666667e-01, 5.98173516e-01, 2.07858048e-01, ...,\n",
       "        3.52622413e-01, 1.00000000e+00, 4.97217180e-02],\n",
       "       [2.70646766e-01, 5.75342466e-01, 1.83776933e-01, ...,\n",
       "        3.70590477e-01, 9.99440878e-01, 4.94619162e-02],\n",
       "       [2.06467662e-01, 5.47945205e-01, 1.62230672e-01, ...,\n",
       "        3.88739533e-01, 9.98881644e-01, 4.92027822e-02],\n",
       "       ...,\n",
       "       [8.06467662e-01, 3.42465753e-01, 6.08365019e-02, ...,\n",
       "        7.16941870e-01, 3.01367755e-04, 9.98131932e-01],\n",
       "       [7.22885572e-01, 3.15068493e-01, 8.49176172e-02, ...,\n",
       "        7.33634314e-01, 1.50482980e-04, 9.99065876e-01],\n",
       "       [6.16417910e-01, 3.05936073e-01, 1.08998733e-01, ...,\n",
       "        7.50000000e-01, 0.00000000e+00, 1.00000000e+00]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler = scaler.fit(df_for_training)\n",
    "df_for_training_scaled = scaler.transform(df_for_training)\n",
    "\n",
    "df_for_training_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainX shape == (2183, 24, 10).\n",
      "trainY shape == (2183, 1).\n"
     ]
    }
   ],
   "source": [
    "#Empty lists to be populated using formatted training data\n",
    "trainX = []\n",
    "trainY = []\n",
    "\n",
    "n_future = 1   # Number of timesteps we want to look into the future based on the past timesteps.\n",
    "n_past = 24  # Number of past timesteps we want to use to predict the future.\n",
    "\n",
    "#Reformat input data into a shape: (n_samples x timesteps x n_features)\n",
    "#In my example, my df_for_training_scaled has a shape (12823, 5)\n",
    "#12823 refers to the number of data points and 5 refers to the columns (multi-variables).\n",
    "for i in range(n_past, len(df_for_training_scaled) - n_future +1):\n",
    "    trainX.append(df_for_training_scaled[i - n_past:i, 0:df_for_training.shape[1]])\n",
    "    trainY.append(df_for_training_scaled[i + n_future - 1:i + n_future, 0])\n",
    "\n",
    "trainX, trainY = np.array(trainX), np.array(trainY)\n",
    "\n",
    "print('trainX shape == {}.'.format(trainX.shape))\n",
    "print('trainY shape == {}.'.format(trainY.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_dates = pd.date_range(start, end, freq='H')\n",
    "n_days_for_prediction = (pd.to_datetime(end) - pd.to_datetime(start)).days\n",
    "n_days_for_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Keras is training/fitting/evaluating on array-like data. Keras may not be optimized for this format, so if your input data format is supported by TensorFlow I/O (https://github.com/tensorflow/io) we recommend using that to load a Dataset instead.\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Cannot convert the argument `type_value`: dtype('<M8[ns]') to a TensorFlow DType.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\57861\\OneDrive - Hafslund Eco\\Skrivebord\\heco.sommer.notebooks\\heco.sommer.notebooks\\LoadModel.ipynb Cell 14'\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/57861/OneDrive%20-%20Hafslund%20Eco/Skrivebord/heco.sommer.notebooks/heco.sommer.notebooks/LoadModel.ipynb#ch0000014?line=0'>1</a>\u001b[0m \u001b[39m#Make prediction\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/57861/OneDrive%20-%20Hafslund%20Eco/Skrivebord/heco.sommer.notebooks/heco.sommer.notebooks/LoadModel.ipynb#ch0000014?line=1'>2</a>\u001b[0m \u001b[39m# predict_dates = predict_dates.reset_index(drop=True)\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/57861/OneDrive%20-%20Hafslund%20Eco/Skrivebord/heco.sommer.notebooks/heco.sommer.notebooks/LoadModel.ipynb#ch0000014?line=2'>3</a>\u001b[0m prediction \u001b[39m=\u001b[39m reconstructed_model\u001b[39m.\u001b[39;49mpredict(predict_dates)\n",
      "File \u001b[1;32mc:\\Users\\57861\\.conda\\envs\\p39_sommer\\lib\\site-packages\\keras\\utils\\traceback_utils.py:67\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:  \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[0;32m     66\u001b[0m   filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n\u001b[1;32m---> 67\u001b[0m   \u001b[39mraise\u001b[39;00m e\u001b[39m.\u001b[39mwith_traceback(filtered_tb) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n\u001b[0;32m     68\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     69\u001b[0m   \u001b[39mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32mc:\\Users\\57861\\.conda\\envs\\p39_sommer\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:721\u001b[0m, in \u001b[0;36mas_dtype\u001b[1;34m(type_value)\u001b[0m\n\u001b[0;32m    718\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(type_value, _dtypes\u001b[39m.\u001b[39mDType):\n\u001b[0;32m    719\u001b[0m   \u001b[39mreturn\u001b[39;00m _INTERN_TABLE[type_value\u001b[39m.\u001b[39mas_datatype_enum]\n\u001b[1;32m--> 721\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCannot convert the argument `type_value`: \u001b[39m\u001b[39m{\u001b[39;00mtype_value\u001b[39m!r}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    722\u001b[0m                 \u001b[39m\"\u001b[39m\u001b[39mto a TensorFlow DType.\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\u001b[1;31mTypeError\u001b[0m: Cannot convert the argument `type_value`: dtype('<M8[ns]') to a TensorFlow DType."
     ]
    }
   ],
   "source": [
    "#Make prediction\n",
    "# predict_dates = predict_dates.reset_index(drop=True)\n",
    "prediction = reconstructed_model.predict(predict_dates) #shape = (n, 1) where n is the n_days_for_prediction"
   ]
  }
 ],
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