REF_DATA_CPS.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "REF_DATA_CPS.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DSybEGcDLkl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b72f90c0-59ea-4db7-ea36-b7de9436ffcb"
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn.preprocessing\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "from keras.layers import Dense,Dropout,SimpleRNN,LSTM\n",
        "from keras.models import Sequential\n",
        "\n",
        "#check all the files in the input dataset\n",
        "print(os.listdir(\"/content/drive/My Drive/CSVNEW\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['WM_hourly.csv', 'VC_hourly.csv', 'tubelights_hourly.csv', 'TOAST_hourly.csv', 'REF_hourly.csv', 'fans_hourly.csv', 'Geyser_hourly (1).csv', 'oven_hourly.csv', 'refregirator_hourly.csv', 'vaccum_cleaner_hourly.csv']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R0maWqw_rpkD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "27bac66d-34c2-4f50-f1ad-fe63f27061bf"
      },
      "source": [
        "fpath='/content/drive/My Drive/CSVNEW/refregirator_hourly.csv'\n",
        "\n",
        "df=pd.read_csv(fpath)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Datetime</th>\n",
              "      <th>refregirator</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020:01:01 00:00:00</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020:01:01 01:00:00</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020:01:01 02:00:00</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020:01:01 03:00:00</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020:01:01 04:00:00</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Datetime  refregirator\n",
              "0  2020:01:01 00:00:00         200.0\n",
              "1  2020:01:01 01:00:00         200.0\n",
              "2  2020:01:01 02:00:00         200.0\n",
              "3  2020:01:01 03:00:00         200.0\n",
              "4  2020:01:01 04:00:00         200.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ViEi5ygse0Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "8178ee2c-b92d-4a82-97ee-1dee05e930c8"
      },
      "source": [
        "df = pd.read_csv(fpath, index_col='Datetime', parse_dates=['Datetime'])\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>refregirator</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Datetime</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020:01:01 00:00:00</th>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020:01:01 01:00:00</th>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020:01:01 02:00:00</th>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020:01:01 03:00:00</th>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020:01:01 04:00:00</th>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     refregirator\n",
              "Datetime                         \n",
              "2020:01:01 00:00:00         200.0\n",
              "2020:01:01 01:00:00         200.0\n",
              "2020:01:01 02:00:00         200.0\n",
              "2020:01:01 03:00:00         200.0\n",
              "2020:01:01 04:00:00         200.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iuF1Dpf8smFa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b08ea18-4aa5-49d6-cb35-4f4772105bd6"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "refregirator    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p5wkaVMjsz2P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "6ece16d0-20d5-4b75-f832-630beec5232e"
      },
      "source": [
        "df.plot(figsize=(16,4),legend=True)\n",
        "plt.title('refregirator hourly power consumption data - BEFORE NORMALIZATION')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tsgvdCYt_hy7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68dee589-e3e0-4c10-8c94-f881c3d4da2a"
      },
      "source": [
        "  def normalize_data(df):\n",
        "    scaler = sklearn.preprocessing.MinMaxScaler()\n",
        "    df['refregirator']=scaler.fit_transform(df['refregirator'].values.reshape(-1,1))\n",
        "    return df\n",
        "\n",
        "df_norm = normalize_data(df)\n",
        "df_norm.shape\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2184, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gw7R02llAcIP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "12153097-c4af-4acf-abdc-3d5b2348f1f3"
      },
      "source": [
        "df_norm.plot(figsize=(16,4),legend=True)\n",
        "plt.title('refregirator hourly power consumption data - AFTER NORMALIZATION')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "20ZJJLW1AF8s"
      },
      "source": [
        "def load_data(stock, seq_len):\n",
        "    X_train = []\n",
        "    y_train = []\n",
        "    for i in range(seq_len, len(stock)):\n",
        "        X_train.append(stock.iloc[i-seq_len : i, 0])\n",
        "        y_train.append(stock.iloc[i, 0])\n",
        "    \n",
        "    #1 last 2000 days are going to be used in test\n",
        "    X_test = X_train[2000:]             \n",
        "    y_test = y_train[2000:]\n",
        "    \n",
        "    #2 first 2000 days are going to be used in training\n",
        "    X_train = X_train[:2000]           \n",
        "    y_train = y_train[:2000]\n",
        "    \n",
        "    #3 convert to numpy array\n",
        "    X_train = np.array(X_train)\n",
        "    y_train = np.array(y_train)\n",
        "    \n",
        "    X_test = np.array(X_test)\n",
        "    y_test = np.array(y_test)\n",
        "    \n",
        "    #4 reshape data to input into RNN models\n",
        "    X_train = np.reshape(X_train, (2000, seq_len, 1))\n",
        "    \n",
        "    X_test = np.reshape(X_test, (X_test.shape[0], seq_len, 1))\n",
        "    \n",
        "    return [X_train, y_train, X_test, y_test]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AyWgzw4eA-1b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fdbeb334-7744-4f90-ea9f-8e2a038b0894"
      },
      "source": [
        "seq_len = 20 #choose sequence length\n",
        "\n",
        "X_train, y_train, X_test, y_test = load_data(df, seq_len)\n",
        "\n",
        "print('X_train.shape = ',X_train.shape)\n",
        "print('y_train.shape = ', y_train.shape)\n",
        "print('X_test.shape = ', X_test.shape)\n",
        "print('y_test.shape = ',y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train.shape =  (2000, 20, 1)\n",
            "y_train.shape =  (2000,)\n",
            "X_test.shape =  (164, 20, 1)\n",
            "y_test.shape =  (164,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xuyEPgZJBJ6f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f573a76-d6f3-468e-ba89-0b12f273a89a"
      },
      "source": [
        "rnn_model = Sequential()\n",
        "\n",
        "rnn_model.add(SimpleRNN(40,activation=\"tanh\",return_sequences=True, input_shape=(X_train.shape[1],1)))\n",
        "rnn_model.add(Dropout(0.15))\n",
        "\n",
        "rnn_model.add(SimpleRNN(40,activation=\"tanh\",return_sequences=True))\n",
        "rnn_model.add(Dropout(0.15))\n",
        "\n",
        "rnn_model.add(SimpleRNN(40,activation=\"tanh\",return_sequences=False))\n",
        "rnn_model.add(Dropout(0.15))\n",
        "\n",
        "rnn_model.add(Dense(1))\n",
        "\n",
        "rnn_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "simple_rnn (SimpleRNN)       (None, 20, 40)            1680      \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 20, 40)            0         \n",
            "_________________________________________________________________\n",
            "simple_rnn_1 (SimpleRNN)     (None, 20, 40)            3240      \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 20, 40)            0         \n",
            "_________________________________________________________________\n",
            "simple_rnn_2 (SimpleRNN)     (None, 40)                3240      \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 40)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 41        \n",
            "=================================================================\n",
            "Total params: 8,201\n",
            "Trainable params: 8,201\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Fgf2xDgBUO3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c358e15c-6ec2-4c85-f338-855576bf4185"
      },
      "source": [
        "rnn_model.compile(optimizer=\"adam\",loss=\"MSE\")\n",
        "rnn_model.fit(X_train, y_train, epochs=20, batch_size=50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.4530\n",
            "Epoch 2/20\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.2477\n",
            "Epoch 3/20\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.1841\n",
            "Epoch 4/20\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.1295\n",
            "Epoch 5/20\n",
            "40/40 [==============================] - 0s 10ms/step - loss: 0.1128\n",
            "Epoch 6/20\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.0860\n",
            "Epoch 7/20\n",
            "40/40 [==============================] - 0s 10ms/step - loss: 0.0684\n",
            "Epoch 8/20\n",
            "40/40 [==============================] - 0s 10ms/step - loss: 0.0599\n",
            "Epoch 9/20\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.0560\n",
            "Epoch 10/20\n",
            "40/40 [==============================] - 0s 10ms/step - loss: 0.0456\n",
            "Epoch 11/20\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.0403\n",
            "Epoch 12/20\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.0379\n",
            "Epoch 13/20\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.0350\n",
            "Epoch 14/20\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.0351\n",
            "Epoch 15/20\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.0302\n",
            "Epoch 16/20\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.0306\n",
            "Epoch 17/20\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.0290\n",
            "Epoch 18/20\n",
            "40/40 [==============================] - 0s 10ms/step - loss: 0.0274\n",
            "Epoch 19/20\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.0252\n",
            "Epoch 20/20\n",
            "40/40 [==============================] - 0s 11ms/step - loss: 0.0240\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f15a91e64e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IaQGcY1YBfYC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7aefd5c-8074-4d1b-e938-d5afdb6be9e3"
      },
      "source": [
        "rnn_predictions = rnn_model.predict(X_test)\n",
        "rnn_score = r2_score(y_test,rnn_predictions)\n",
        "print(\"R2 Score of RNN model = \",rnn_score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2 Score of RNN model =  -0.3554748217128274\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0IKQwRoBlk6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "26bdc6af-5c28-4180-b335-d453e69a7d97"
      },
      "source": [
        "def plot_predictions(test, predicted, title):\n",
        "    plt.figure(figsize=(16,4))\n",
        "    plt.plot(test, color='blue',label='Actual power consumption data')\n",
        "    plt.plot(predicted, alpha=0.7, color='orange',label='Predicted power consumption data')\n",
        "    plt.title(title)\n",
        "    plt.xlabel('Last 164 hours(6 days) prediction of energy consumption from two month 91 days data ')\n",
        "    plt.ylabel('Normalized power consumption scale')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    \n",
        "plot_predictions(y_test, rnn_predictions, \"REFREGIRATOR ENERGY CONSUMPTION Predictions made by simple RNN model\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tXnMj4SnBvGT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9080717-fe3c-491b-8f7f-afc93bb7f587"
      },
      "source": [
        "lstm_model = Sequential()\n",
        "\n",
        "lstm_model.add(LSTM(40,activation=\"tanh\",return_sequences=True, input_shape=(X_train.shape[1],1)))\n",
        "lstm_model.add(Dropout(0.15))\n",
        "\n",
        "lstm_model.add(LSTM(40,activation=\"tanh\",return_sequences=True))\n",
        "lstm_model.add(Dropout(0.15))\n",
        "\n",
        "lstm_model.add(LSTM(40,activation=\"tanh\",return_sequences=False))\n",
        "lstm_model.add(Dropout(0.15))\n",
        "\n",
        "lstm_model.add(Dense(1))\n",
        "\n",
        "lstm_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm (LSTM)                  (None, 20, 40)            6720      \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 20, 40)            0         \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 20, 40)            12960     \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 20, 40)            0         \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 40)                12960     \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 40)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 41        \n",
            "=================================================================\n",
            "Total params: 32,681\n",
            "Trainable params: 32,681\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fp5PYnmrB1jK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f394a01-0629-4975-cc11-2855d42c92ca"
      },
      "source": [
        "lstm_model.compile(optimizer=\"adam\",loss=\"MSE\")\n",
        "lstm_model.fit(X_train, y_train, epochs=20, batch_size=50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "40/40 [==============================] - 1s 21ms/step - loss: 0.1268\n",
            "Epoch 2/20\n",
            "40/40 [==============================] - 1s 20ms/step - loss: 0.0196\n",
            "Epoch 3/20\n",
            "40/40 [==============================] - 1s 21ms/step - loss: 0.0187\n",
            "Epoch 4/20\n",
            "40/40 [==============================] - 1s 21ms/step - loss: 0.0163\n",
            "Epoch 5/20\n",
            "40/40 [==============================] - 1s 21ms/step - loss: 0.0168\n",
            "Epoch 6/20\n",
            "40/40 [==============================] - 1s 21ms/step - loss: 0.0156\n",
            "Epoch 7/20\n",
            "40/40 [==============================] - 1s 21ms/step - loss: 0.0158\n",
            "Epoch 8/20\n",
            "40/40 [==============================] - 1s 22ms/step - loss: 0.0154\n",
            "Epoch 9/20\n",
            "40/40 [==============================] - 1s 22ms/step - loss: 0.0159\n",
            "Epoch 10/20\n",
            "40/40 [==============================] - 1s 22ms/step - loss: 0.0148\n",
            "Epoch 11/20\n",
            "40/40 [==============================] - 1s 21ms/step - loss: 0.0146\n",
            "Epoch 12/20\n",
            "40/40 [==============================] - 1s 21ms/step - loss: 0.0146\n",
            "Epoch 13/20\n",
            "40/40 [==============================] - 1s 20ms/step - loss: 0.0140\n",
            "Epoch 14/20\n",
            "40/40 [==============================] - 1s 21ms/step - loss: 0.0132\n",
            "Epoch 15/20\n",
            "40/40 [==============================] - 1s 21ms/step - loss: 0.0151\n",
            "Epoch 16/20\n",
            "40/40 [==============================] - 1s 21ms/step - loss: 0.0141\n",
            "Epoch 17/20\n",
            "40/40 [==============================] - 1s 21ms/step - loss: 0.0137\n",
            "Epoch 18/20\n",
            "40/40 [==============================] - 1s 21ms/step - loss: 0.0139\n",
            "Epoch 19/20\n",
            "40/40 [==============================] - 1s 21ms/step - loss: 0.0134\n",
            "Epoch 20/20\n",
            "40/40 [==============================] - 1s 21ms/step - loss: 0.0138\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f159f86c5c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X5JFWluCB9Kw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4308b18a-ef9d-4dee-ffd3-792ca54019f0"
      },
      "source": [
        "lstm_predictions = lstm_model.predict(X_test)\n",
        "lstm_score = r2_score(y_test, lstm_predictions)\n",
        "print(\"R^2 Score of LSTM model = \",lstm_score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R^2 Score of LSTM model =  -0.024059929677437975\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22pEtW3XCB6Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "2fcb8921-98e1-4ebb-88be-97023b7e7fe6"
      },
      "source": [
        "plot_predictions(y_test, lstm_predictions, \"REFREGIRATOR ENERGY CONSUMPTION Predictions made by LSTM model\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Ge-8YhGCJOp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "outputId": "afd7f20c-5507-4001-e76a-cf0398b80d95"
      },
      "source": [
        "plt.figure(figsize=(15,8))\n",
        "\n",
        "plt.plot(y_test, c=\"orange\", linewidth=3, label=\"Original values\")\n",
        "plt.plot(lstm_predictions, c=\"red\", linewidth=3, label=\"LSTM predictions\")\n",
        "plt.plot(rnn_predictions, alpha=0.5, c=\"green\", linewidth=3, label=\"RNN predictions\")\n",
        "plt.legend()\n",
        "plt.xlabel('Last 164 hours(6 days) prediction of energy consumption from two month 91 days data ')\n",
        "plt.ylabel('Normalized power consumption scale')\n",
        "plt.title(\"Predictions vs actual data\", fontsize=20)\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}