Exp12-WA-Prediction Wind Approach 1 Batch 1 48 hours ahead.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from pandas import DataFrame\n",
    "from pandas import Series\n",
    "from pandas import concat\n",
    "from pandas import read_csv\n",
    "from pandas import datetime\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from math import sqrt\n",
    "from matplotlib import pyplot\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hardcode all variables\n",
    "window_size = 48\n",
    "batch_size_exp = 1\n",
    "epoch_exp = 10\n",
    "neurons_exp = 50\n",
    "predict_values_exp = 6000\n",
    "lag_exp=48"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# frame a sequence as a supervised learning problem\n",
    "def timeseries_to_supervised(data, lag=1):\n",
    "    df = DataFrame(data)\n",
    "    columns = [df.shift(i) for i in range(1, lag+1)]\n",
    "    columns.append(df)\n",
    "    df = concat(columns, axis=1)\n",
    "    df.fillna(0, inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a differenced series\n",
    "def difference(dataset, interval=1):\n",
    "    diff = list()\n",
    "    for i in range(interval, len(dataset)):\n",
    "        value = dataset[i] - dataset[i - interval]\n",
    "        diff.append(value)\n",
    "    return Series(diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# invert differenced value\n",
    "def inverse_difference(history, yhat, interval=1):\n",
    "    return yhat + history[-interval]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale train and test data to [-1, 1]\n",
    "def scale(data_norm):\n",
    "    scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "    scaler = scaler.fit(data_norm)\n",
    "    # transform train\n",
    "    data_norm = data_norm.reshape(data_norm.shape[0], data_norm.shape[1])\n",
    "    data_scaled = scaler.transform(data_norm)\n",
    "    return scaler, data_scaled\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inverse scaling for a forecasted value\n",
    "def invert_scale(scaler, X, value):\n",
    "    new_row = [x for x in X] + [value]\n",
    "    array = np.array(new_row)\n",
    "    array = array.reshape(1, len(array))\n",
    "    inverted = scaler.inverse_transform(array)\n",
    "    return inverted[0, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit an LSTM network to training data\n",
    "from keras.layers import Activation, Dense, BatchNormalization, TimeDistributed\n",
    "def fit_lstm(train, batch_size, nb_epoch, neurons):\n",
    "    X, y = train[:, 0:-1], train[:, -1]\n",
    "    X = X.reshape(X.shape[0], 1, X.shape[1])\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(neurons_exp, dropout = 0.1 ,batch_input_shape=(batch_size, X.shape[1], X.shape[2]), stateful=True))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dense(50))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dense(50))\n",
    "    model.add(Activation('tanh'))\n",
    "    model.add(Dense(1))\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "    for i in range(nb_epoch):\n",
    "        model.fit(X, y, epochs=1, batch_size=batch_size, verbose=1, shuffle=False)\n",
    "        model.reset_states()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a one-step forecast\n",
    "def forecast_lstm(model, batch_size, X):\n",
    "    X = X.reshape(1, 1, len(X))\n",
    "    #print(X)\n",
    "    yhat = model.predict(X, batch_size=1)\n",
    "    return yhat[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pressure | (atm)</th>\n",
       "      <th>Wind direction | (deg)</th>\n",
       "      <th>Wind speed | (m/s)</th>\n",
       "      <th>Power generated by system | (kW)</th>\n",
       "    </tr>\n",
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      "text/plain": [
       "                     Pressure | (atm)  Wind direction | (deg)  \\\n",
       "DateTime                                                        \n",
       "2007-01-01 00:00:00          0.890467                     104   \n",
       "2007-01-01 01:00:00          0.890583                     106   \n",
       "2007-01-01 02:00:00          0.890366                     102   \n",
       "2007-01-01 03:00:00          0.890326                      99   \n",
       "2007-01-01 04:00:00          0.890549                      98   \n",
       "\n",
       "                     Wind speed | (m/s)  Power generated by system | (kW)  \n",
       "DateTime                                                                   \n",
       "2007-01-01 00:00:00               3.429                              0.00  \n",
       "2007-01-01 01:00:00               3.579                              0.00  \n",
       "2007-01-01 02:00:00               4.307                           1986.68  \n",
       "2007-01-01 03:00:00               4.562                           2597.61  \n",
       "2007-01-01 04:00:00               4.553                           2555.46  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''' Loading data '''\n",
    "import pandas as pd\n",
    "series = pd.read_excel('WA_WIND_DATA.xlsx',index_col=\"DateTime\")\n",
    "series.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Power generated by system | (kW)</th>\n",
       "    </tr>\n",
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       "      <th>DateTime</th>\n",
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      "text/plain": [
       "                     Power generated by system | (kW)\n",
       "DateTime                                             \n",
       "2007-01-01 00:00:00                              0.00\n",
       "2007-01-01 01:00:00                              0.00\n",
       "2007-01-01 02:00:00                           1986.68\n",
       "2007-01-01 03:00:00                           2597.61\n",
       "2007-01-01 04:00:00                           2555.46"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Drop all the features as we will not be having any in production'''\n",
    "#del series['Air temperature | (\\'C)']\n",
    "del series['Pressure | (atm)']\n",
    "del series['Wind speed | (m/s)']\n",
    "del series['Wind direction | (deg)']\n",
    "series.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Power generated by system | (kW)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-31 09:00:00</th>\n",
       "      <td>2688.91</td>\n",
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       "      <th>2009-12-31 13:00:00</th>\n",
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      "text/plain": [
       "                     Power generated by system | (kW)\n",
       "DateTime                                             \n",
       "2009-12-31 09:00:00                           2688.91\n",
       "2009-12-31 10:00:00                              0.00\n",
       "2009-12-31 11:00:00                              0.00\n",
       "2009-12-31 12:00:00                              0.00\n",
       "2009-12-31 13:00:00                           1554.51"
      ]
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0,10):\n",
    "  series = series[:-1]\n",
    "series.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform data to be stationary\n",
    "raw_values = series.values\n",
    "diff_values = difference(raw_values, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform data to be supervised learning\n",
    "supervised = timeseries_to_supervised(diff_values, lag_exp)\n",
    "supervised_values = supervised.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype object was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# split data into train and test-sets\n",
    "scaler,supervised_values = scale(supervised_values)\n",
    "train_scaled, test_scaled = supervised_values[0:-predict_values_exp], supervised_values[-predict_values_exp:]\n",
    "#print(test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "20269/20269 [==============================] - 55s 3ms/step - loss: 0.0107\n",
      "Epoch 1/1\n",
      "20269/20269 [==============================] - 54s 3ms/step - loss: 0.0107\n",
      "Epoch 1/1\n",
      "20269/20269 [==============================] - 53s 3ms/step - loss: 0.0107\n",
      "Epoch 1/1\n",
      "20269/20269 [==============================] - 55s 3ms/step - loss: 0.0107\n",
      "Epoch 1/1\n",
      "20269/20269 [==============================] - 54s 3ms/step - loss: 0.0107\n",
      "Epoch 1/1\n",
      "20269/20269 [==============================] - 53s 3ms/step - loss: 0.0107\n",
      "Epoch 1/1\n",
      "20269/20269 [==============================] - 54s 3ms/step - loss: 0.0107\n",
      "Epoch 1/1\n",
      "20269/20269 [==============================] - 54s 3ms/step - loss: 0.0107\n",
      "Epoch 1/1\n",
      "20269/20269 [==============================] - 54s 3ms/step - loss: 0.0107\n",
      "Epoch 1/1\n",
      "20269/20269 [==============================] - 54s 3ms/step - loss: 0.0107\n"
     ]
    }
   ],
   "source": [
    "# fit the model\n",
    "lstm_model = fit_lstm(train_scaled, batch_size_exp, epoch_exp, neurons_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# walk-forward validation on the test data\n",
    "predictions = list()\n",
    "expectations = list()\n",
    "for i in range(len(test_scaled)-window_size):\n",
    "    window_prediction_frame = test_scaled\n",
    "    test_pred = list()\n",
    "    for j in range(window_size):\n",
    "        X, y = window_prediction_frame[i, 0:-1], window_prediction_frame[i, -1]\n",
    "        yhat = forecast_lstm(lstm_model, 1, X)#batch_size_exp to 1\n",
    "        '''# Start Debug prints\n",
    "        print(\"X: %\", X)\n",
    "        print(\"yhat: %\", yhat)\n",
    "        # End Debug prints'''\n",
    "        # Replacing value in test scaled with the predicted value.\n",
    "        test_pred = [yhat] + test_pred \n",
    "        if len(test_pred) > lag_exp+1:\n",
    "            test_pred = test_pred[:-1]\n",
    "        if j+1<len(window_prediction_frame):\n",
    "            if j+1 > lag_exp+1:\n",
    "                window_prediction_frame[j+1] = test_pred\n",
    "            else:\n",
    "                window_prediction_frame[j+1] = np.concatenate((test_pred, window_prediction_frame[j+1, j+1:]),axis=0)\n",
    "\n",
    "    # invert scaling\n",
    "    yhat = invert_scale(scaler, X, yhat)\n",
    "    # invert differencing\n",
    "    yhat = inverse_difference(raw_values, yhat, len(test_scaled)+1-i)\n",
    "    # store forecast\n",
    "    expected = raw_values[len(train_scaled) + i + 1 + window_size]\n",
    "    predictions.append(yhat)\n",
    "    expectations.append(expected)\n",
    "    #print('Hour=%d, Predicted=%f, Expected=%f' % (i+1, yhat, expected))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Percent Error:  117.5917610273375\n"
     ]
    }
   ],
   "source": [
    "expectations = np.array(expectations)\n",
    "predictions = np.array(predictions)\n",
    "print(\"Mean Absolute Percent Error: \", (np.mean(np.abs((expectations - predictions) / expectations.mean()))*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# line plot of observed vs predicted\n",
    "fig = pyplot.figure(figsize=(20,8))\n",
    "pyplot.plot(expectations[500:1000], label=\"True\")\n",
    "pyplot.plot(predictions[500:1000], label=\"Predicted\")\n",
    "pyplot.legend(loc='upper right')\n",
    "pyplot.xlabel(\"Number of hours\")\n",
    "pyplot.ylabel(\"Power generated by system (kW)\")\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}