{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 12.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fundamentals of Solar Cells and Photovoltaic Systems Engineering**\n",
    "\n",
    "**Solutions Manual - Chapter 12**\n",
    "\n",
    "**Problem 12.5**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The tabulated data provided in the online repository of this book includes hourly irradiance data for the different solar irradiance components measured in Madrid, Spain, on June 10 at a fixed plane at a tilt of 35⁰, which is very close to the optimum inclination (for maximum energy capture throughout the year) at such latitude.**\n",
    "\n",
    "**(a) For every hour, estimate the diffuse irradiance from the global and direct components and compare them to the experimentally measured diffuse irradiance. Calculate the root-mean-square error (RMSE) between both time series.**\n",
    "\n",
    "**(b) What are the typical instruments used to obtain such experimental data?**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the package [pandas](https://pandas.pydata.org/) to handle the data and [matplotlib.pyplot](https://matplotlib.org/stable/index.html) to plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by importing the measured solar irradiance components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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>Gb(i)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time(UTC+1)</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07:00</th>\n",
       "      <td>5.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08:00</th>\n",
       "      <td>131.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09:00</th>\n",
       "      <td>291.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10:00</th>\n",
       "      <td>439.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11:00</th>\n",
       "      <td>569.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12:00</th>\n",
       "      <td>633.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13:00</th>\n",
       "      <td>653.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14:00</th>\n",
       "      <td>610.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15:00</th>\n",
       "      <td>533.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16:00</th>\n",
       "      <td>423.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17:00</th>\n",
       "      <td>286.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18:00</th>\n",
       "      <td>140.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19:00</th>\n",
       "      <td>21.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23:00</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Gb(i)\n",
       "time(UTC+1)        \n",
       "00:00          0.00\n",
       "01:00          0.00\n",
       "02:00          0.00\n",
       "03:00          0.00\n",
       "04:00          0.00\n",
       "05:00          0.00\n",
       "06:00          0.00\n",
       "07:00          5.90\n",
       "08:00        131.87\n",
       "09:00        291.56\n",
       "10:00        439.15\n",
       "11:00        569.09\n",
       "12:00        633.52\n",
       "13:00        653.63\n",
       "14:00        610.45\n",
       "15:00        533.22\n",
       "16:00        423.96\n",
       "17:00        286.50\n",
       "18:00        140.95\n",
       "19:00         21.13\n",
       "20:00          0.00\n",
       "21:00          0.00\n",
       "22:00          0.00\n",
       "23:00          0.00"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G = pd.read_csv('data/Global_35deg.csv', index_col=0, header=0) \n",
    "B = pd.read_csv('data/Direct_35deg.csv', index_col=0, header=0) \n",
    "D = pd.read_csv('data/Diffuse_35deg.csv', index_col=0, header=0) \n",
    "B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can estimated the diffuse irradiance $D$ by substracting the direct irradiance $B$ from the global irradiance $G$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time(UTC+1)\n",
       "00:00      0.00\n",
       "01:00      0.00\n",
       "02:00      0.00\n",
       "03:00      0.00\n",
       "04:00      0.00\n",
       "05:00      0.00\n",
       "06:00     13.34\n",
       "07:00     57.23\n",
       "08:00    106.95\n",
       "09:00    147.89\n",
       "10:00    183.18\n",
       "11:00    206.35\n",
       "12:00    233.26\n",
       "13:00    244.38\n",
       "14:00    244.52\n",
       "15:00    234.24\n",
       "16:00    211.69\n",
       "17:00    172.14\n",
       "18:00    124.94\n",
       "19:00     69.31\n",
       "20:00     24.39\n",
       "21:00      0.00\n",
       "22:00      0.00\n",
       "23:00      0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D_estimated = G['G(i)']-B['Gb(i)']\n",
    "D_estimated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the measured and estimated irradiance components. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1fcb09d9888>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(G['G(i)'], \n",
    "         linewidth=2, label='Global')\n",
    "plt.plot(B['Gb(i)'], \n",
    "         linewidth=2, label='Direct')\n",
    "plt.plot(D['Gd(i)'],\n",
    "         linewidth=2, label='Diffuse (measured)')\n",
    "plt.plot(D_estimated,\n",
    "         linewidth=2, linestyle='--',label='Diffuse (estimated)')\n",
    "plt.ylabel('Irradiance (W/m2)')\n",
    "plt.xticks(rotation=70)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculate the root-mean square error (RMSE) between the estimated and measured diffuse irradiance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RMSE is 8.41 W/m2\n"
     ]
    }
   ],
   "source": [
    "RMSE=np.sqrt(((D['Gd(i)']-D_estimated)**2).mean())\n",
    "print('The RMSE is ' + str(RMSE.round(2)) + ' W/m2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(b) What are the typical instruments used to obtain such experimental data?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Global irradiance is measured using a pyranometer. Direct irradiance using a pyrheliometer. Diffuse irradiance can be measured using a pyranometer together with an element to block direct irradiance. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
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