models/SES.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['value', 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.6000000000000001, 0.7000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.5, 0.6000000000000001, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.6000000000000001, 0.5, 0.5, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.5, 0.6000000000000001, 0.5, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.7000000000000001, 0.7000000000000001, 0.6000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.7000000000000001, 0.8, 0.7000000000000001, 0.8, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.8, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.8, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.8, 0.7000000000000001, 0.8, 0.8, 0.8, 0.7000000000000001, 0.8, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.8, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.9, 0.9, 0.8, 0.8, 0.8, 0.8, 0.9, 0.9, 0.9, 0.8, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9]\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "dict={}\n",
    "dates = ['01-10-2017.csv','02-10-2017.csv','03-10-2017.csv','04-10-2017.csv','05-10-2017.csv','06-10-2017.csv','07-10-2017.csv','08-10-2017.csv','09-10-2017.csv','10-10-2017.csv','11-10-2017.csv','12-10-2017.csv','13-10-2017.csv','14-10-2017.csv','15-10-2017.csv','16-10-2017.csv','17-10-2017.csv','18-10-2017.csv','19-10-2017.csv','20-10-2017.csv','21-10-2017.csv','22-10-2017.csv','23-10-2017.csv','24-10-2017.csv','25-10-2017.csv','26-10-2017.csv','27-10-2017.csv','28-10-2017.csv','29-10-2017.csv','30-10-2017.csv']\n",
    "for i in range(len(dates)):\n",
    "    dict[dates[i]] = []\n",
    "    time = []\n",
    "    for d in csv.DictReader(open(dates[i]), delimiter=','):\n",
    "        time.append(d['time'])\n",
    "        dict[dates[i]].append(float(d['value']))\n",
    "n = len(dict[dates[len(dates)-1]])\n",
    "m = len(dates)\n",
    "alpha  = [0]*n\n",
    "alphamin = ['value']+[0.1]*n\n",
    "forecast =[[0]*n for i in range(m)]\n",
    "forecast[1] = [dict[dates[0]][j] for j in range(n)]\n",
    "for j in range(n):\n",
    "    mse = [0]*9\n",
    "    for k in range(1,10):\n",
    "        alpha[j] = k*0.1\n",
    "        for i in range(2,m):\n",
    "            forecast[i][j] = (alpha[j]*dict[dates[i-1]][j]) + ((1 - alpha[j])*forecast[i-1][j])\n",
    "        for i in range(1,m):\n",
    "            mse[k-1] += (forecast[i][j] - dict[dates[i]][j])**2\n",
    "    min = mse[0]\n",
    "    for i in range(1,9):\n",
    "        if mse[i]<min:\n",
    "            min = mse[i]\n",
    "            alphamin[j+1] = (i+1)*0.1\n",
    "print(alphamin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "16:25  :  3316.6  :  3246.925877470571\n",
      "16:30  :  3284.71  :  3276.376311634907\n",
      "16:35  :  3268.3  :  3221.221366884894\n",
      "16:40  :  3303.83  :  3263.50958573734\n",
      "16:45  :  3298.64  :  3255.2872694657117\n",
      "16:50  :  3286.65  :  3256.296256254328\n",
      "16:55  :  3289.83  :  3252.4332375075996\n",
      "17:00  :  3325.41  :  3282.2282777041573\n",
      "17:05  :  3331.15  :  3276.2190873303057\n",
      "17:10  :  3360.35  :  3310.338984042042\n",
      "17:15  :  3361.65  :  3306.555112793001\n",
      "17:20  :  3401.7  :  3340.4921135513014\n",
      "17:25  :  3396.77  :  3321.993191182175\n",
      "17:30  :  3444.21  :  3350.732318406741\n",
      "17:35  :  3451.03  :  3397.7642708977296\n",
      "17:40  :  3467.93  :  3400.0296363827642\n",
      "17:45  :  3478.56  :  3445.2244074293676\n",
      "17:50  :  3534.62  :  3447.074452840684\n",
      "17:55  :  3562.07  :  3526.082491710505\n",
      "18:00  :  3601.83  :  3536.3873717066112\n",
      "18:05  :  3601.81  :  3542.4487285056493\n",
      "18:10  :  3610.39  :  3538.948105062316\n",
      "18:15  :  3594.51  :  3554.380019017847\n",
      "18:20  :  3606.88  :  3556.6751973994405\n",
      "18:25  :  3613.85  :  3546.9528503862575\n",
      "18:30  :  3585.76  :  3553.62046789581\n",
      "18:35  :  3605.61  :  3583.0595652185843\n",
      "18:40  :  3563.32  :  3545.5258305876923\n",
      "18:45  :  3575.95  :  3516.461953524862\n",
      "18:50  :  3573.43  :  3524.505026489949\n",
      "18:55  :  3544.47  :  3516.2058791089135\n",
      "19:00  :  3514.83  :  3513.21031036687\n",
      "19:05  :  3531.16  :  3507.7771463209347\n",
      "19:10  :  3492.37  :  3483.423115750663\n",
      "19:15  :  3498.46  :  3450.394113177107\n",
      "19:20  :  3468.52  :  3443.406332776015\n",
      "19:25  :  3435.12  :  3452.142484975182\n",
      "19:30  :  3418.42  :  3400.876771804951\n",
      "19:35  :  3394.83  :  3402.434547321217\n",
      "19:40  :  3381.0  :  3389.0357719556696\n",
      "19:45  :  3365.75  :  3383.6982408196413\n",
      "19:50  :  3320.13  :  3351.970252151955\n",
      "19:55  :  3300.47  :  3357.595077078999\n",
      "20:00  :  3279.81  :  3313.4496025345543\n",
      "20:05  :  3218.08  :  3270.65661890084\n",
      "20:10  :  3225.94  :  3256.3388852577964\n",
      "20:15  :  3210.6  :  3239.621484696494\n",
      "20:20  :  3179.57  :  3174.0760682098967\n",
      "20:25  :  3169.78  :  3209.3975380862767\n",
      "20:30  :  3124.26  :  3180.1434059318185\n",
      "20:35  :  3054.92  :  3139.937208389782\n",
      "20:40  :  3070.65  :  3132.7796491768427\n",
      "20:45  :  3031.57  :  3083.8202092231795\n",
      "20:50  :  3020.53  :  3038.27408395185\n",
      "20:55  :  2974.74  :  3045.038175695475\n",
      "21:00  :  2924.98  :  2980.0386882839675\n",
      "21:05  :  2894.82  :  2918.806790628907\n",
      "21:10  :  2851.23  :  2925.932293521137\n",
      "21:15  :  2846.35  :  2912.531807113893\n",
      "21:20  :  2835.66  :  2902.387578375805\n",
      "21:25  :  2834.72  :  2888.2076951905347\n",
      "21:30  :  2807.64  :  2895.1587688194245\n",
      "21:35  :  2819.69  :  2878.870339681198\n",
      "21:40  :  2789.08  :  2874.523097633572\n",
      "21:45  :  2782.95  :  2867.1905412300544\n",
      "21:50  :  2783.25  :  2837.271487524449\n",
      "21:55  :  2768.8  :  2839.468965501423\n",
      "22:00  :  2725.62  :  2821.884446800877\n",
      "22:05  :  2711.74  :  2792.818262072399\n",
      "22:10  :  2684.13  :  2792.862020196303\n",
      "22:15  :  2659.83  :  2762.67053114674\n",
      "22:20  :  2646.25  :  2741.438311390116\n",
      "22:25  :  2643.3  :  2722.646621380248\n",
      "22:30  :  2625.33  :  2730.5049681176724\n",
      "22:35  :  2599.46  :  2716.6591497016716\n",
      "22:40  :  2602.57  :  2704.10989961353\n",
      "22:45  :  2573.63  :  2666.950762666873\n",
      "22:50  :  2551.76  :  2647.7966859042485\n",
      "22:55  :  2548.19  :  2668.10432538233\n",
      "23:00  :  2520.56  :  2623.154169757112\n",
      "23:05  :  2515.78  :  2629.2474327349446\n",
      "23:10  :  2471.9  :  2588.7988343756183\n",
      "23:15  :  2480.82  :  2570.3117976982217\n",
      "23:20  :  2447.98  :  2551.400353804696\n",
      "23:25  :  2430.25  :  2538.6641304313225\n",
      "23:30  :  2414.38  :  2520.8321510821997\n",
      "23:35  :  2391.89  :  2514.5261397231798\n",
      "23:40  :  2390.34  :  2504.150305006684\n",
      "23:45  :  2375.75  :  2472.551009544742\n",
      "23:50  :  2364.92  :  2456.912222111562\n",
      "23:55  :  2333.93  :  2439.683841422095\n",
      "971.2428262589463\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "dict={}\n",
    "data = ['11-10-2017.csv','12-10-2017.csv','13-10-2017.csv','14-10-2017.csv','15-10-2017.csv','16-10-2017.csv','17-10-2017.csv','18-10-2017.csv','19-10-2017.csv','20-10-2017.csv','21-10-2017.csv','22-10-2017.csv','23-10-2017.csv','24-10-2017.csv','25-10-2017.csv','26-10-2017.csv','27-10-2017.csv','28-10-2017.csv','29-10-2017.csv','30-10-2017.csv','31-10-2017.csv']\n",
    "for i in range(len(data)):\n",
    "    dict[data[i]] = []\n",
    "    time = []\n",
    "    for d in csv.DictReader(open(data[i]), delimiter=','):\n",
    "        time.append(d['time'])\n",
    "        dict[data[i]].append(float(d['value']))\n",
    "n = len(dict[data[len(data)-1]])\n",
    "alpha = [0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.6000000000000001, 0.7000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.5, 0.6000000000000001, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.6000000000000001, 0.5, 0.5, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.5, 0.6000000000000001, 0.5, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.7000000000000001, 0.7000000000000001, 0.6000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.7000000000000001, 0.8, 0.7000000000000001, 0.8, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.8, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.8, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.8, 0.7000000000000001, 0.8, 0.8, 0.8, 0.7000000000000001, 0.8, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.8, 0.7000000000000001, 0.7000000000000001, 0.7000000000000001, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.9, 0.9, 0.8, 0.8, 0.8, 0.8, 0.9, 0.9, 0.9, 0.8, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9]\n",
    "forecast={}\n",
    "forecast[data[1]] = [dict[data[0]][i] for i in range(len(dict[data[1]]))]\n",
    "for j in range(2,len(data)):\n",
    "    forecast[data[j]] = []\n",
    "    for i in range(n):\n",
    "        forecast[data[j]].append((alpha[i]*dict[data[j-1]][i]) + ((1 - alpha[i])*forecast[data[j-1]][i]))\n",
    "for i in range(n):\n",
    "    print(time[i],\" : \",dict[data[len(data)-1]][i],\" : \",forecast[data[len(data)-1]][i])\n",
    "mse = 0\n",
    "for i in range(n):\n",
    "    mse += (dict[data[len(data)-1]][i] - forecast[data[len(data)-1]][i])**2\n",
    "print((mse)**0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tjn9TjCP9hk1mvLszC8eH4Gxrav0Ykx49/dzKRvuAz4rX2iRyD2o9ig58AzWl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\nVeb/Ac7fzy8GY4QxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179d5b18390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dict[data[len(data)-1]])\n",
    "plt.plot(forecast[data[len(data)-1]])\n",
    "plt.legend(['load', 'forecast'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}