PostAnalysis1-get-15min-loads.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This module aggregate the 1-second data to 15-minute data as the utility companies calculate the bill each 15 minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "from settings import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load data and extract time series of solar power and building load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_res = os.path.join(\"results\", \"load_with_BESS.csv\")\n",
    "data = pd.read_csv(path_res)\n",
    "\n",
    "dts = np.array(data['datetime'])\n",
    "PSs = np.array(data['solar_power'])\n",
    "LBs = np.array(data['building_load'])\n",
    "BESS_outputs = np.array(data['BESS_output'])\n",
    "\n",
    "# define start and end date, as well as the target aggregation time interval\n",
    "myd_start = \"2022-05-31\"\n",
    "myd_end = \"2022-06-01\"\n",
    "dt_start = datetime.strptime(f\"{myd_start} 00:00:00\", \"%Y-%m-%d %H:%M:%S\")\n",
    "dt_end = datetime.strptime(f\"{myd_end} 23:59:59\", \"%Y-%m-%d %H:%M:%S\")\n",
    "billing_interval = timedelta(minutes = 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Aggregate\n",
    "For each 15-minute time window, collect data points falls within the window and calculate the mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-05-31 00:00:00\n",
      "2022-05-31 02:47:52\n",
      "2022-05-31 05:35:35\n",
      "2022-05-31 08:23:16\n",
      "2022-05-31 11:11:10\n",
      "2022-05-31 13:59:04\n",
      "2022-05-31 16:46:50\n",
      "2022-05-31 19:34:45\n",
      "2022-05-31 22:22:26\n",
      "2022-06-01 01:28:03\n",
      "2022-06-01 04:15:55\n",
      "2022-06-01 07:03:47\n",
      "2022-06-01 09:51:35\n",
      "2022-06-01 12:39:22\n",
      "2022-06-01 15:27:11\n",
      "2022-06-01 18:15:08\n",
      "2022-06-01 21:03:01\n",
      "2022-06-01 23:50:57\n",
      "Finished\n"
     ]
    }
   ],
   "source": [
    "time_window = [dt_start, dt_start + billing_interval]\n",
    "PS_window = []\n",
    "LB_window = []\n",
    "BESS_output_window = []\n",
    "PSs_15min = []\n",
    "LBs_15min = []\n",
    "BESS_outputs_15min = []\n",
    "time_points_15min = []\n",
    "\n",
    "N = len(dts)\n",
    "\n",
    "for i in range(N):\n",
    "    dt = datetime.strptime(dts[i], \"%Y%m%d-%H%M%S\")\n",
    "    if i+1 < N:\n",
    "        dt_next = datetime.strptime(dts[i+1], \"%Y%m%d-%H%M%S\")\n",
    "    else:\n",
    "        dt_next = dt_end + timedelta(seconds = 1)\n",
    "    \n",
    "    if dt >= time_window[0] and dt < time_window[1]:\n",
    "        PS_window.append(PSs[i])\n",
    "        LB_window.append(LBs[i])\n",
    "        BESS_output_window.append(BESS_outputs[i])\n",
    "        \n",
    "    if dt_next >= time_window[1]:\n",
    "        time_window_0_str = datetime.strftime(time_window[0], \"%Y%m%d-%H%M%S\")\n",
    "        time_points_15min.append(time_window_0_str)\n",
    "        if len(PS_window) < 1:\n",
    "            time_window[0] = time_window[1]\n",
    "            time_window[1] += billing_interval\n",
    "            print(\"Here\")\n",
    "            continue\n",
    "        PS = np.mean(PS_window)\n",
    "        LB = np.mean(LB_window)\n",
    "        BESS_output = np.mean(BESS_output_window)\n",
    "        \n",
    "        PSs_15min.append(PS)\n",
    "        LBs_15min.append(LB)\n",
    "        BESS_outputs_15min.append(BESS_output)\n",
    "        \n",
    "        # update new bins\n",
    "        time_window[0] = time_window[1]\n",
    "        time_window[1] += billing_interval\n",
    "        PS_window = []\n",
    "        LB_window = []\n",
    "        BESS_output_window = []\n",
    "    \n",
    "    if i % 10000 == 0:\n",
    "        print(dt)\n",
    "print(\"Finished\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Output results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>solar_power</th>\n",
       "      <th>building_load</th>\n",
       "      <th>BESS_output</th>\n",
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       "      <td>-0.000622</td>\n",
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       "      <th>...</th>\n",
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       "      <td>32.659900</td>\n",
       "      <td>7.660854</td>\n",
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       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>20220601-230000</td>\n",
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       "      <td>8.041156</td>\n",
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       "      <th>189</th>\n",
       "      <td>20220601-231500</td>\n",
       "      <td>-0.002451</td>\n",
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      "text/plain": [
       "            datetime  solar_power  building_load  BESS_output\n",
       "0    20220531-000000     0.000000      26.920135     2.167686\n",
       "1    20220531-001500    -0.000291      26.402160     1.801587\n",
       "2    20220531-003000    -0.000109      22.853849     0.004226\n",
       "3    20220531-004500     0.000000      24.502675     0.311718\n",
       "4    20220531-010000    -0.000622      26.726524     1.884406\n",
       "..               ...          ...            ...          ...\n",
       "187  20220601-224500    -0.000954      32.659900     7.660854\n",
       "188  20220601-230000    -0.002003      33.039153     8.041156\n",
       "189  20220601-231500    -0.002451      31.009239     6.011690\n",
       "190  20220601-233000    -0.001734      31.956579     6.958312\n",
       "191  20220601-234500    -0.001066      31.508437     6.509502\n",
       "\n",
       "[192 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_export = {'datetime': time_points_15min,\\\n",
    "               'solar_power': PSs_15min,\\\n",
    "               'building_load': LBs_15min,\\\n",
    "               'BESS_output': BESS_outputs_15min}\n",
    "df = pd.DataFrame(info_export)\n",
    "path_res = os.path.join(\"results\", f\"load_with_BESS_15min.csv\")\n",
    "df.to_csv(path_res)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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