LSTM_Multivariate_Energy_Harvesting.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LSTM_Multivariate_Energy_Harvesting",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1pNx4tgiowexzw_wGFrrJTIWIoopHlruQ",
      "authorship_tag": "ABX9TyMetq4e+4PB43sKew9sd+P+",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/prakashece/Deep_learning_algorithms/blob/master/LSTM_Multivariate_Energy_Harvesting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2UkLeatTKgJ"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qNmb9qwYTW0I",
        "outputId": "d8bc24df-6d8f-4ef6-fba4-7e7cd449f215"
      },
      "source": [
        "df=pd.read_csv('/content/drive/My Drive/Solar_mutivariate.csv')\n",
        "print(df.shape)\n",
        "#print(df)\n",
        "train_data=df.iloc[1:385343,1:4].values\n",
        "print(train_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(385440, 4)\n",
            "[[   0.   1010.     97.94]\n",
            " [   0.   1010.     96.49]\n",
            " [   0.   1010.     96.47]\n",
            " ...\n",
            " [   0.   1000.     92.05]\n",
            " [   0.   1000.     98.82]\n",
            " [   0.   1000.     98.4 ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QdFheZbhWccS",
        "outputId": "6ce02a9a-06e6-455d-bcd6-cdb1b48c2910"
      },
      "source": [
        "train_x=[]\n",
        "train_y=[]\n",
        "for i in range(48,385342):\n",
        "  train_x.append(train_data[i-48:i,0:train_data.shape[1]])\n",
        "  train_y.append(train_data[i,0])\n",
        "train_x,train_y=np.array(train_x),np.array(train_y)\n",
        "#train_x=np.reshape(train_x,(train_x.shape[0],train_x.shape[1],1))\n",
        "print(train_x.shape)\n",
        "print(train_y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(385294, 48, 3)\n",
            "(385294,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpGcwPNXYweG"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Dropout\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QayPSbDwZEFU"
      },
      "source": [
        "regressor=Sequential()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fcE3RN8rZjuI"
      },
      "source": [
        "regressor.add(LSTM(units=48,return_sequences=True,input_shape=(train_x.shape[1],train_x.shape[2])))\n",
        "regressor.add(Dropout(0.05))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TelM8HVOaI23"
      },
      "source": [
        "regressor.add(LSTM(units=48,return_sequences=True))\n",
        "regressor.add(Dropout(0.05))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZTy1h3yalmG"
      },
      "source": [
        "regressor.add(LSTM(units=48,return_sequences=True))\n",
        "regressor.add(Dropout(0.05))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1FsiQbJWapDj"
      },
      "source": [
        "regressor.add(LSTM(units=48))\n",
        "regressor.add(Dropout(0.05))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "li2mkUjLatZi"
      },
      "source": [
        "regressor.add(Dense(units=1))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDS5kpnHa56a"
      },
      "source": [
        "regressor.compile(optimizer='adam',loss='mean_squared_error')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yzjqSwIGbIGb",
        "outputId": "5268f140-d206-451e-9a5f-be0666205898"
      },
      "source": [
        "regressor.fit(train_x,train_y,epochs=5,batch_size=32)\n",
        "print(regressor.loss)\n",
        "#model.save('/content/drive/My Drive/solar_model.h5')\n",
        "regressor.save('/content/drive/My Drive/solar_multivariate_model.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "12041/12041 [==============================] - 561s 46ms/step - loss: 0.0820\n",
            "Epoch 2/5\n",
            "12041/12041 [==============================] - 556s 46ms/step - loss: 0.0460\n",
            "Epoch 3/5\n",
            "12041/12041 [==============================] - 555s 46ms/step - loss: 0.0429\n",
            "Epoch 4/5\n",
            "12041/12041 [==============================] - 557s 46ms/step - loss: 0.0414\n",
            "Epoch 5/5\n",
            "12041/12041 [==============================] - 555s 46ms/step - loss: 0.0411\n",
            "mean_squared_error\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FaxigFxyebMf"
      },
      "source": [
        "#regressor.save('/content/drive/My Drive/solar_model.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YHhYhBqv1zwP",
        "outputId": "2c2f404b-bc69-4d97-ebc7-0587564d093e"
      },
      "source": [
        "from keras.models import load_model\n",
        "model=load_model(\"/content/drive/My Drive/solar_multivariate_model.h5\")\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_4 (LSTM)                (None, 48, 48)            9984      \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 48, 48)            0         \n",
            "_________________________________________________________________\n",
            "lstm_5 (LSTM)                (None, 48, 48)            18624     \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 48, 48)            0         \n",
            "_________________________________________________________________\n",
            "lstm_6 (LSTM)                (None, 48, 48)            18624     \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 48, 48)            0         \n",
            "_________________________________________________________________\n",
            "lstm_7 (LSTM)                (None, 48)                18624     \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 48)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 49        \n",
            "=================================================================\n",
            "Total params: 65,905\n",
            "Trainable params: 65,905\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-xb1KLXemH8",
        "outputId": "6282974a-aa16-4326-a318-1abc4a033855"
      },
      "source": [
        "#test_data= df.iloc[385344:,1:2].values #for 96 values\n",
        "#test_data= df.iloc[385439:,1:2].values #for 48 values\n",
        "test_data= df.iloc[385344:385392,1:2].values #for middle 48 values\n",
        "#data_test=pd.DataFrame(test_data)\n",
        "#test_data= df.iloc[7248:7296,1:2].values #summer solar power(48 values)\n",
        "data_test=pd.DataFrame(test_data)\n",
        "print(len(data_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "48\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QqNK-CW9bY19",
        "outputId": "aab83c5f-a281-433c-d289-384137d50307"
      },
      "source": [
        "data_total=df.iloc[:,1:4].values\n",
        "data_total=pd.DataFrame(data_total)\n",
        "print(data_total.shape)\n",
        "#inputs=data_total[385392:].values\n",
        "#inputs=data_total[380832:].values #for 96 values \n",
        "#inputs=data_total[383136:].values\n",
        "inputs=data_total[385296:385392].values #for middle 48 values\n",
        "print(inputs.shape)\n",
        "test_x=[]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(385440, 3)\n",
            "(96, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fq1fKKPfbsAv",
        "outputId": "24c598b1-51b9-4db6-d2a6-3ae21b4baaf6"
      },
      "source": [
        "for i in range(48,96): # for midddle 48 values\n",
        "  test_x.append(inputs[i-48:i,0:inputs.shape[1]])\n",
        "#test_x.append(inputs)\n",
        "test_x=np.array(test_x)\n",
        "#test_x=np.reshape(test_x,(test_x.shape[0],test_x.shape[1],))\n",
        "print(test_x.shape)\n",
        "print(test_x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(48, 48, 3)\n",
            "[[[   0.   1000.    100.  ]\n",
            "  [   0.   1000.    100.  ]\n",
            "  [   0.   1000.    100.  ]\n",
            "  ...\n",
            "  [   0.   1000.     98.82]\n",
            "  [   0.   1000.     98.4 ]\n",
            "  [   0.   1000.     98.39]]\n",
            "\n",
            " [[   0.   1000.    100.  ]\n",
            "  [   0.   1000.    100.  ]\n",
            "  [   0.   1000.    100.  ]\n",
            "  ...\n",
            "  [   0.   1000.     98.4 ]\n",
            "  [   0.   1000.     98.39]\n",
            "  [   0.   1000.     97.98]]\n",
            "\n",
            " [[   0.   1000.    100.  ]\n",
            "  [   0.   1000.    100.  ]\n",
            "  [   0.   1000.    100.  ]\n",
            "  ...\n",
            "  [   0.   1000.     98.39]\n",
            "  [   0.   1000.     97.98]\n",
            "  [   0.   1000.    100.  ]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[   0.   1000.     98.82]\n",
            "  [   0.   1000.     98.4 ]\n",
            "  [   0.   1000.     98.39]\n",
            "  ...\n",
            "  [   0.   1000.     98.29]\n",
            "  [   0.   1000.     98.26]\n",
            "  [   0.   1000.     97.92]]\n",
            "\n",
            " [[   0.   1000.     98.4 ]\n",
            "  [   0.   1000.     98.39]\n",
            "  [   0.   1000.     97.98]\n",
            "  ...\n",
            "  [   0.   1000.     98.26]\n",
            "  [   0.   1000.     97.92]\n",
            "  [   0.   1000.     97.91]]\n",
            "\n",
            " [[   0.   1000.     98.39]\n",
            "  [   0.   1000.     97.98]\n",
            "  [   0.   1000.    100.  ]\n",
            "  ...\n",
            "  [   0.   1000.     97.92]\n",
            "  [   0.   1000.     97.91]\n",
            "  [   0.   1000.     97.97]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-HZzlsMJbfqG",
        "outputId": "47b9ddca-9b7b-4649-c136-8f68b0a086e5"
      },
      "source": [
        "predict_values=model.predict(test_x)\n",
        "print(predict_values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-1.3955235e-03]\n",
            " [-1.3470352e-03]\n",
            " [-1.2826324e-03]\n",
            " [-1.2468696e-03]\n",
            " [-1.3327897e-03]\n",
            " [-1.2807548e-03]\n",
            " [-1.2119710e-03]\n",
            " [-1.0821819e-03]\n",
            " [-9.0381503e-04]\n",
            " [-6.7850947e-04]\n",
            " [-4.2957067e-04]\n",
            " [ 1.4129281e-04]\n",
            " [-7.0843101e-04]\n",
            " [ 1.5711784e-04]\n",
            " [-7.4958801e-04]\n",
            " [ 7.2709918e-03]\n",
            " [ 2.7357399e-02]\n",
            " [ 5.6839705e-02]\n",
            " [ 1.7779317e-01]\n",
            " [ 2.6034158e-01]\n",
            " [ 2.6508909e-01]\n",
            " [ 2.5721014e-01]\n",
            " [ 5.1199400e-01]\n",
            " [ 7.5574458e-01]\n",
            " [ 1.0145780e+00]\n",
            " [ 1.1927943e+00]\n",
            " [ 1.1234090e+00]\n",
            " [ 9.8598659e-01]\n",
            " [ 7.4168491e-01]\n",
            " [ 6.5655994e-01]\n",
            " [ 5.2617085e-01]\n",
            " [ 3.1833109e-01]\n",
            " [ 2.6047999e-01]\n",
            " [ 1.4388609e-01]\n",
            " [ 4.1125029e-02]\n",
            " [-2.0914078e-03]\n",
            " [-1.0207504e-02]\n",
            " [-9.4198883e-03]\n",
            " [-8.5489154e-03]\n",
            " [-6.3061416e-03]\n",
            " [-5.5218339e-03]\n",
            " [-4.7421157e-03]\n",
            " [-4.2544901e-03]\n",
            " [-3.9084256e-03]\n",
            " [-3.6236942e-03]\n",
            " [-3.3647716e-03]\n",
            " [-3.1715930e-03]\n",
            " [-2.9893816e-03]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQJqF-ycdhjv"
      },
      "source": [
        "The values for prediction is 48 values from test data. The test data has 96 values in total, From that i have used first 96 values for predicting the next 48 sequence."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "1E-P38ZSj-Wb",
        "outputId": "b744b8df-a0d7-4fdb-cc8e-d4a125b8b016"
      },
      "source": [
        "plt.plot(data_test,color='red',label='test data')\n",
        "plt.plot(predict_values,color='blue',label='predicted data')\n",
        "plt.legend()\n",
        "plt.xlabel(\"Time (hours)\")\n",
        "plt.ylabel(\"Solar Power (Watts)\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "id": "ahyhYqol33x7",
        "outputId": "0745270d-d258-4ad5-c47d-fe2acc4a6676"
      },
      "source": [
        "x=np.array(predict_values*1000)\n",
        "print(x.shape)\n",
        "#print(\"Predicted solar power in mW for next 30 minutes\",x)\n",
        "time_val=[]\n",
        "limit_val=[]\n",
        "battery_voltage=3.7\n",
        "battery_current=0.050\n",
        "Energy=battery_voltage*battery_current*1000\n",
        "print(\"Energy from battery in mWH\",Energy)\n",
        "power_req_1=50\n",
        "power_req_2=25\n",
        "power_req_3=10\n",
        "print(\"Max Power required in mW\",power_req_1)\n",
        "battery_time_1=Energy/power_req_1*60\n",
        "battery_time_2=Energy/power_req_2*60\n",
        "battery_time_3=Energy/power_req_3*60\n",
        "print(\"Battery life time in minutes\",battery_time_1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(48, 1)\n",
            "Energy from battery in mWH 185.00000000000003\n",
            "Max Power required in mW 50\n",
            "Battery life time in minutes 222.00000000000003\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "qoQ1_VGxc_eX",
        "outputId": "38a454a0-00d6-4bab-edf2-888340a3653f"
      },
      "source": [
        "limit_val=[]\n",
        "time_val=[]\n",
        "limit_val_2=[]\n",
        "time_val_2=[]\n",
        "Energy=185\n",
        "Energy1=185\n",
        "dec=0.83\n",
        "for i in range(1,222):\n",
        "  time_val_2.append(i)\n",
        "  Energy1=Energy1-0.83\n",
        "  limit_val_2.append(Energy1)\n",
        "plt.plot(time_val_2[1:48],limit_val_2[1:48])\n",
        "##########################\n",
        "for i in range(0,47):\n",
        "  print(i)\n",
        "  time_val.append(i)\n",
        "  if(i==0):\n",
        "    limit_val= 185-0.83\n",
        "  else:\n",
        "    limit_val[i]= limit_val[i-1]-dec\n",
        "'''\n",
        "  limit_val[i]+=x[i]\n",
        "  if limit_val[i]>185:\n",
        "    limit_val[i].append(185.0)\n",
        "  else:\n",
        "    limit_val[i].append(limit_val[i])\n",
        "'''\n",
        "   #print(limit_val[i])\n",
        "plt.plot(time_val,limit_val)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-3b70b008e3de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0mlimit_val\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m185\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.83\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m     \u001b[0mlimit_val\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mlimit_val\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mdec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m '''\n\u001b[1;32m     22\u001b[0m   \u001b[0mlimit_val\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'float' object is not subscriptable"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "fgCaruLodTw_",
        "outputId": "c980cf7f-8c05-4651-a8ca-dd4d4e87cfe9"
      },
      "source": [
        "#battery time in 30 minutes range \n",
        "print(x.shape)\n",
        "print('solar_val',x[16:23])\n",
        "limit_val_1=[]\n",
        "time_val_1=[]\n",
        "Energy=185\n",
        "for i in range(1,8):\n",
        "  time_val_1.append(i)\n",
        "  Energy=Energy-25\n",
        "  limit_val_1.append(Energy)\n",
        "print(limit_val_1)\n",
        "plt.plot(time_val_1,limit_val_1)\n",
        "for i in range(1,7):\n",
        "  if x[i+16]>185 and limit_val_1[i]<185:\n",
        "    limit_val_1[i]+=25*i\n",
        "    if limit_val_1[i]>185:\n",
        "      limit_val_1[i]=185.0\n",
        "print(limit_val_1[6])\n",
        "plt.plot(time_val_1,limit_val_1)     \n",
        "plt.show() \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(48, 1)\n",
            "solar_val [[  5.476594]\n",
            " [106.784584]\n",
            " [184.95525 ]\n",
            " [272.0708  ]\n",
            " [297.67822 ]\n",
            " [297.05722 ]\n",
            " [474.33115 ]]\n",
            "[160, 135, 110, 85, 60, 35, 10]\n",
            "160\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iQrZERCId3Qh"
      },
      "source": [
        "I used battery energy = 185mwh and consumption value as 50mw. \n",
        "Formula used = E/P = 185/50= 3.7 (222.0 minutes).\n",
        "So the battery can withstand for 222 minutes to provide 50mw.\n",
        "for every minute the energy loss would be 0.83w using 185/222 value.\n",
        "Graph-1: shows energy drop vs time.\n",
        "Graph-2: energy recharge w r to solar power vs time"
      ]
    }
  ]
}