SolarPanelEstimator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# <span style=\"color:rgb(213,80,0)\">Solar Panel Output Estimator</span>\n",
    "\n",
    "Specify the location of your panels.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "% Calculate Solar Time\n",
    "location = [40.714 -74.006 -5];\n",
    "lambda = location(2);                                      % Longitude\n",
    "phi = location(1);                                         % Latitude\n",
    "UTCoff = location(3);                                      % UTC offset\n",
    "if(UTCoff < 0)\n",
    "    TZ = ['UTC' num2str(UTCoff)];\n",
    "else\n",
    "    TZ = ['UTC+' num2str(UTCoff)];\n",
    "end\n",
    "january1  = datetime(2016,1,1,'TimeZone',TZ);              % January 1st\n",
    "localYear = 2018;\n",
    "localMonth = 6;\n",
    "localDay = 1;\n",
    "localHour = 12;\n",
    "localTime = datetime(localYear,localMonth,localDay,localHour,0,0,'TimeZone',TZ);\n",
    "d = caldays(between(january1,localTime,'Day'));            % Day of year\n",
    "solarCorr = solarCorrection(d,lambda,UTCoff);              % Correction to local time\n",
    "solarTime = localTime + minutes(solarCorr);\n",
    "% Calculate Solar Declination and Elevation\n",
    "delta = asind(sind(23.45)*sind(360*(d - 81)/365));          % Declination\n",
    "omega = 15*(solarTime.Hour + solarTime.Minute/60 - 12);     % Hour angle\n",
    "alpha = asind(sind(delta)*sind(phi) + ...                   % Elevation\n",
    "     cosd(delta)*cosd(phi)*cosd(omega));\n",
    "% Calculate Air Mass and Solar Radiation\n",
    "AM = 1/(cosd(90-alpha) + 0.50572*(6.07955+alpha)^-1.6354);\n",
    "sRad = 1.353*0.7^(AM^0.678);                                % kW/m^2\n",
    "% Calculate Solar Radiation on Fixed Panels\n",
    "gamma = acosd((sind(delta)*cosd(phi) - cosd(delta)*sind(phi)*cosd(omega))/cosd(alpha));\n",
    "if (hour(solarTime) >= 12) && (omega >= 0)\n",
    "    gamma = 360 - gamma;\n",
    "end\n",
    "beta = 180;                                                 % Panel azimuth\n",
    "tau = 35;                                                   % Panel tilt\n",
    "pRad = sRad*max(0,(cosd(alpha)*sind(tau)*cosd(beta-gamma) + sind(alpha)*cosd(tau)));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Specify the panel size and efficiency value.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "% Calculate Panel Size and Efficiency\n",
    "pSize = 24;                          % Panel size in m^2\n",
    "eff = 30;                         % Panel efficiency\n",
    "pElec = eff*pSize*pRad;                                    % Panel electric output in kW\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected electrical output = 645.7519 kW\n"
     ]
    }
   ],
   "source": [
    "disp(['Expected electrical output = ' num2str(pElec) ' kW'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+5QDVT6H8dPz4aADtIauBqeNQQdZ9yk/Hv+QNQENWA5NjUmlMHRRwwFfsympyuTw/P7+lpcXNzc3JyUmTRaRSaX5+PiHEw8PDwcFBzwECV6E0YYACDvjETKFQGDuG/1NRUREZGTlgwIB+/fr98ssvcXFx06dPZ16kpKRk3rx5gwcP7t2798mTJ5OSksaOHdv+bRKJpKysTD9RA3thH90dKOCgPU7sS1mU1ZYsWWJra7t27VpCSElJSURExOnTp/v27cuwyBtvvDF8+PD4+HhCyI8//rhixYqioiKhUNjmbZz4nwAdovfI2B3rBA4OQIkT+1K2jEBSFJWXl7d9+3b6qaenp6OjY3FxcYe1l9L169eXLVtGPx47dmxcXFx+fn5QUJDewwW2Qj7TOZyBA25hS1ZrbGyUyWQ+Pj7Z2dklJSWJiYlisbi0tJQ5q4lEoqamJvoxRVECgeDx48cGiRfYBXtbw2hzBg5rG1iILVlNJpMRQkQiUUFBQU5OTmJiopmZWUNDA/NSU6dO/eabb9zc3Hr37r1p0yYbGxuKojp8p0QioR+wv3yGLsHu1SjoFY6VbwqUO0+uYEtWs7CwIITU1NTExMSEhYURQiiKcnR0ZF4qISFh48aNU6dOtbe3j4mJKS4utra27vCdSGb8w6ddavmbjoQQ1++qjB1I1yC3mQLVnScnMhyLZosMHz58zZo1oaGh9FMfH5+kpKRRo0ZpuHhtbW1AQEBeXl77nwRw4gwnaIhPg410MiN/5TOO5jYacpsp4MS+lC21GiEkPDw8NTV15MiR5ubm6enp1tbWqvM+EhIScnJy2qzQ+Ph4oVD4+eefUxS1fv36V199VcNfuQEX8Wa/2SaZKXE6t6FuA5ZgUVaLjY1NSEjw9fUViUR2dnbJyckCQSc3NV22bNm7777r5eVFCPHy8vriiy8MEikYGj/2leqSWRvIbQDdwaIRSP3hRNUM7fFjsFHDZMawLOdyG40f/32gihP7UhbVagBK/DjY735O4kHdRvjyvwlcgawG7MKDPWB3irMOcTq3EQxLgmEhqwFbcH2vp/Nk1oYyt3ExsRHkNjAUZDUwPk7v6fSdzNpw/a6Ku0UbQW4D/UNWAyMz++AUF/duBk5mqrg+IEmQ20CfkNXAmLiY0liSTpDbADqErAbGwdF9GdtOa/Ept3HuywDshKwGRsDRXRjbUpoSP3IbRw90gG2Q1cCgsOfSH67nNhRtoBPIamA4nN5hsbZQa4MHuY3T3xMwuk4utAigK5zeVXElpSm5flel/A0A59CJTXnBLYAuQa0Gesf1UUfOpTQlOrFxMXiMRoLWUKuBftE7Ju7umziaFfhBOYUEQHOo1UBfuF6iEV6kNO6WazTMjYSuQlYDvcDYEXvwILERfKNAYxiBBN3jxw6I05mgDe7OHFHCaCRoCLUa6BJvBov4lNJ4A6ORoAlkNdAZfpRohKcpjevjkDSMRkKnMAIJOkD/uogfOxoe7PrV4cE4JA2jkcAAtRp0F2/yGeF1SqPxo2IjGI0E9VCrQbfwKaUBt9C/g0TRBm0gq4GW+DTqSONHEdMp3oxD0pDYoA2MQII2eJbPiMmkNBpvxiFpGI0EVajVoMuQ0niAfxUbijagsatWk8vl+fn5LS0tbm5uTk5OmizS1NR07tw5iqJeeOGFgQMH6jtCQEoD1sJdbICwqlarqKgYNWrUzp07jx8/HhYWdvDgwU4XuXjxYnBw8LFjx06dOhUVFbVu3ToDxGnKsMvgE56VazTcxQaIgjUWL168cuVK+vGVK1fc3NwePnzIvEhkZOS2bdvox5WVla6urrdv327/NldXV92GaprI+yeNHYLulYU/a+wQjIyva4CXX1ej48S+lC21GkVReXl5Y8aMoZ96eno6OjoWFxczL6VQKGxtbenHNjY2dDt6jRP4BGOPhKcVG8HcSBPGlqzW2Ngok8l8fHyys7PXrFlDCBGLxaWlpcxLxcbG7tq1KyMj4/jx47GxsbNmzRo0aJBB4jU5/Bt7RErjPSQ208SWrCaTyQghIpGooKBg//79hBAzM7OGhgbmpezt7fv373/u3LmTJ0+2trYOGDDAELGaHqQ0fuNruUaQ2EwSW+ZAWlhYEEJqampiYmLCwsIIIRRFOTp2sqXNmzdvzpw5kZGRhJC6urpXX31VIpH4+/u3f6dEIqEflJWV6Th0vkNKMwU8+wUb6JBy58kVbMlqlpaWdnZ2JSUloaGhYrGYEFJUVDRt2jSGRVpbW+/du6cccrS1tXV1db13716Hb0Yy0w7/UhqYGkz37ybVnScnMhxbRiAJIeHh4ampqVKplBCSnp5ubW0dFBSk/GtCQkKbFSoSiQYMGHDmzBn66a1bt3777TdnZ2cDhsxzvNwXoCJRB+OQwA8symqxsbEODg6+vr4+Pj779u1LTk4WCDoJb+vWrfn5+aGhobNmzYqMjFy2bNnLL79smGh5DynNBCGxAQ+YKRQKY8egdxKJBCOQXYKUZsr4uqJ4+a02ME7sS1lUqwHoD1/31KA5lGsmAlkN2uLfIS1SWpdgHBI4DVkN/oZ/KQ20gMQG3IWsBv8fL1MaCjXt8DixAb8hq8H/QUoDE4Fyjd+Q1YAQpDToCI/LNSQ2HkNWA36mNNAJJDbgHGQ1U8fXlIZCTVd4nNiAl5DVAMBEoVzjJWQ1k4ZCDTTB43INiY1/kNVMF19TGugDEhtwBbKaieJxSkOhpic8TmzAJ8hqpojHKQ1ACyjX+ARZzeTwO6WhUNMrHpdrSGy8gaxmWvid0gC6A4mNH5DVTAjvUxoKNQPgcbkG/ICsBgDwf1Cu8QCymqlAoQa6wu9yDYmN65DVTALvUxqADiGxcRqyGv+ZQkpDoWZg/C7XgNOQ1XjOFFIagM6hXOMuZDXgPBRqRsH7cg2JjaOQ1fgMhRpAdyCxcRGyGnAbCjUj4n25BlyErMZbKNQAug/lGuf0MHYAfyOXy/Pz81taWtzc3JycnDp9f2FhoVwuV31FIpH07dtXbwECu6BQMzq6XOP3/wKd2HCMyBUsymoVFRWRkZEDBgzo16/f8uXL4+Lipk+fzrzI/v37m5ublU/Pnj27devWUaNG6TlSDsBGCKBDSGwcwqKslpSUFBQUtHbtWkJISUlJRERESEgIc+H11VdfKR9nZ2f/+eefSGmmg/clAleYQrkGHMKW82oUReXl5Y0ZM4Z+6unp6ejoWFxcrOHitbW1n3/++aeffqq3ALkEB5UAOocTbFzBlqzW2Ngok8l8fHyys7PXrFlDCBGLxaWlpRouvnXr1pCQELFYrM8YgUVQHLCKiUyGRGLjBB2MQNbX1zc3N9vb2ytfuXnzZlcTjEwmI4SIRKKCgoKcnJzExEQzM7OGhgZNlq2qqjpw4MBPP/3E8B6JREI/KCsr61JgnINCDQB0SLnz5Iru1mrffPPNyy+//Nprr73xxhvKF2fPnt3VdiwsLAghNTU1MTExu3btIoRQFOXoqNHRX3Z29vDhw5977jmG95T9pauBAQuhUGMhlGt8VabC2LFopLtZbfv27aWlpRcvXgwJCXnzzTe1bsfS0tLOzq6kpEQsFgcEBBBCioqKBg0apMmyx48fDw0N1bprPjGFQg0pDQAYdCurURRlbW0tFAoJIQsXLvTz8/voo4+0bi08PDw1NVUqlRJC0tPTra2tg4KClH9NSEjosBBubW29cePG4MGDte4XAHQC5RqwQbeymkAgUCgUFy9epJ/Gx8c3NTV9/vnn2rUWGxvr4ODg6+vr4+Ozb9++5ORkgaDz8MrLywkhbm5u2nXKJyjUAADMFApFd5a/efPm9OnTjx8/rpwtEhcX98MPP1y/fl0X4emGRCLhyohwdyCrARuYyP+RKWxu7XFiX9rd82pisfjixYuqEyCTkpIKCwu72Sx0lSlsYyayuwSA7tDL79VsbW310SwAsJ+JnF0D1tLXFbMePXpEz/tQpeFMfegqFGoABoYrQ7KWXrLaxIkTy8vL28z1EAgE165d00d3AMAquDIkGJHus5pcLi8vLy8uLsY4JOgKdpHAQijX2En359WEQuGUKVOOHDny6NGjur/TeV9ATGP4ETgHZ9fAWPQyAmltbb127Vr6njJKAoGAVdP9gStQqAGA5nSf1VpbW/ft25ebm6vJzayhm1CoARgRBiFZSPcjkCKRKCgo6O7duyKRyOrvdN4X8B4KNe7CICQYhV5GIG/evLlw4cI2L2IEUudwkAhgdCjX2EYvWW3//v36aBZMDQo1rsMUfzA8vWQ1/NraAHB4CMASKNdYRZfn1YYPH3748GEdNgimDMf4/ICza2BgusxqGzduXLt2rb+///nz53XYLLSHA0MAgA7pMqsFBgZevHgxNjY2MjJy1KhRlZWVOmwcTAoKNT4xhXINtxJlD93P7I+MjCwrK3v99deDg4NnzJiBS4roHAo1AAB19HInGkJIfHz85cuX+/bt6+vrGx8f39raqqeOgH9QqPEPyjUwGH1lNUKIpaXltm3b8vPzy8vLPTw8vvzyS/31ZTpQqAEAMNBjVqPZ29vn5OTk5uYeOnQIo5HQKRRqfGUK5Rqwgd6zGk0sFp89exb3pukmFGoAbIZBSDbQ8a+whw8fbmFh0bt376lTpwYEBDz11FN9+vTRbRfAYyjU+A2XGgED0HGt9s033zz77LNSqXT79u2TJ0/29/eXSCTDhw8fN27cJ598otu+TA0KNQD2Q7lmdDqu1by9vQ8cOKB82tra+ueff964cSM5Ofm7775rc8c1AFU4ijcFKNdA3/RyHUhCSGVl5YEDB/79739XVVU5OTnFxMRgDmR3oFAD4ApcFtK49JXVpk2bNnDgwM8//9zPz0/zpeRyeX5+fktLi5ubm+Y3HS0tLa2srHRwcPD09NQqWDA+HL+bDpRroFf6mgOZnZ0dGhr68ccfSySS4ODgHTt2VFdXMy9SUVExatSonTt3Hj9+PCws7ODBg5p0FBcXt2TJkuPHj7/33ns4dQcAYOoU+tTU1FRZWXn69OmYmJjBgwczv3nx4sUrV66kH1+5csXNze3hw4fMixw5ciQkJKSxsVGhUDx48GDhwoV1dXXt3+bq6qpV+GxB3j9p7BD0qyz8WWOHAIbG+/90Xm62nNiX6ngE8vz580lJSX/++Wd9ff2ff/4pk8kIIXZ2dk899dSbb77JsCBFUXl5edu3b6efenp6Ojo6FhcXjx07lmGpf//73xEREVZWVoQQe3v7bdu26e6jAAAA9+g4qy1ZsqRnz569e/detGiRh4eH5r9Xa2xslMlkPj4+2dnZJSUliYmJYrG4tLSUOasVFhZ6eXmFhYXduXPHzs4uISGB+f1chNPOwEu8P7uGOSPGouOsdvbsWe0WpKs6kUhUUFCQk5OTmJhoZmbW0NDAvFRLS0tubm5KSoqDg8PJkyffe+89sVg8aNAg7WIAo+D3rg0ADEwvs0Xi4+PbvNLU1PTKK68wLGJhYUEIqampiYmJ2bVrFyGEoihHx06uGtezZ8+5c+c6ODgQQkaPHu3u7n7u3LkO3yn5i+afgg1wrAc8hitDcoJEhbFj0YhestrVq1ejoqKUT3/88Udvb28vLy+GRSwtLe3s7EpKSsRicUBAACGkqKio06rLx8enzdRKkUjU4TvL/qLpZwCDQKEGPMaP64yUqTB2LBrRS1Y7duyYXC6fNm0aIeSjjz5asmTJhg0bOp3KER4enpqaKpVKCSHp6enW1tZBQUHKvyYkJLQ/Upg0adLu3burqqoIIYWFhdeuXRs2bJiOP4zxoFAD3kO5Bjqnr19h79u3b8mSJf7+/oSQ8+fP29jYdLpIbGxsQkKCr6+vSCSys7NLTk4WCDpJuhMnTrxz587YsWPNzc2FQuH69esHDhyomw8AANBtmDNieGYKhUJXbcnl8javfPbZZ7///ntaWhr9VCgU6qqvLpFIJFypnZVMYUvA8CPQ+P1N4NO2zIl9qS5rtWHDhnU4a9HNzY0QIhAIrl+/rsPuAADYD+XGz2+ZAAAgAElEQVSagekyqx05ckSHrZkyU9gG+H14DgDGosuspvn1iAEAlHj/i2wwJH1d3Ri0ZgqFGoBJ4ccUf65AVgMjwIE5tIEp/qAryGrsgkINgJdQrhkMshoYGgo1ANAfZDUWQaEGpgyDkKATyGpgUCjUwGRhENIwkNUAgC1QrkH3IasBABgIyjUDQFZjC1M4qYbhRwDQN2Q1AGAR3g9ColzTN2Q1MBAUagBgAMhqAMAuvC/XQK+Q1VjBFE6qAQANg5B6hawGhoDhR+gSlGugNUNkterq6tGjR1dUVBigLwAA9kO5pj8GqtXu3bsXEhIycuTIS5cuGaZHYA8UagBgMIbIavb29qWlpT/++KOfn997770XFRVlgE45BCfVANrDICRoR5f3wmYgFAoHDhy4adMmQohUKjVMpwAArEUPQuKIVueMMFvE3Nzc8J2CsWD4EbSGcg20gDmQAADGgTkj+qCvEcg1a9acO3dOoVAoXzEzMzt27JieugN2QqEGAAaml1otODj40KFDAoHA/O/00RfXYWAdgAHvByFRrumc7ms1uVxeWVlZUlKiRRqTy+X5+fktLS1ubm5OTk6dvv+PP/7473//q3wqEAj8/f272ikAAPCG7rOaUCikq7SuLlhRUREZGTlgwIB+/fotX748Li5u+vTpzIukpaXl5+eLxWL6aY8ePZDV2APDj6ATdLmG7xJoSC/n1SIiIrKyssLDw7u0VFJSUlBQ0Nq1awkhJSUlERERISEhffv2ZVikubl55MiRq1ev7la4AADAF3rJauXl5fv379+yZYutra3yRYFAcPToUXWLUBSVl5e3fft2+qmnp6ejo2NxcfHYsWMZOpJKpRKJRFdhGx6PT6rh4BpAQ/jhmm7pZbZIS0vL4MGD7ezseqgQCoUMizQ2NspkMh8fn+zs7DVr1hBCxGJxaWkpc0fNzc2//fbb5MmThw4dGhkZef78eV1+DABgB97PGQEd0kutdujQoa4uIpPJCCEikaigoCAnJycxMdHMzKyhoYF5qcGDBzc1NSUkJNja2u7evfvtt9/Ozc11dnbWLmwAAOA6A10xq1MWFhaEkJqampiYmLCwMEIIRVGOjp0cnb3//vvKx7GxsXl5eadOnZozZ077dyoHKsvKynQWNKiB4UfQOcwZMRbOneXRV1bbvHlzRkZGXV0dIaRPnz7h4eHx8fEM77e0tLSzsyspKQkNDaXnNBYVFU2bNo1hEYqifv31Vy8vLysrK/oVMzMzdeOcLExmGEkHAPZT3XlyIsPp5bxaQkLCjh075s2bl5ubm5ubu2DBgvT09ISEBOalwsPDU1NT6Wsfp6enW1tbBwUFqbbZZoUKBIIVK1Z8+eWX9NOTJ0+WlZWNGDFC158GugYH1ABdhd9i65BearUffvjh0qVLyhJKLBbPnDnTw8Nj48aNDEvFxsYmJCT4+vqKRCI7O7vk5GSBoJOku3Xr1ri4uIMHDwqFQmtr6y1btgwaNEhnHwMA2ASDkKAJ3We11tZWc3NzZUqjiUSip59+ur6+3sbGRt2C5ubmysKrvY0bN7ZPiu7u7ri2JAAAKOl+BFIkErW0tNTW1qq+KJVKHz58yJDSgB9wKA16hSn+0Cm9jEDOmDHD398/Ojp6ypQphJDjx4/v2LFj0qRJ+uiLozBVBABAH/SS1VauXNm7d+/9+/fv2rWLEGJrazt//vzY2Fh99AXsgUINDICvZ9dwhRFd0dfM/tjYWKQxAAAwMMPdC7uqim/HVqCKl4fPAMA5Bspqcrl89OjRhumL/TDOANAdmDMCDAxXqwEAAOibLs+rXbp0iRDi7e1NP1All8t12BGwDYYfwcB4OWcEE0Z0QpdZ7Z133nnxxRf37dsXHR1NX60YAADAkHSZ1S5cuKB8fOrUKdXLi8jlcg8PDx32BezBv0NmAOAunFczNIwwAOgE5oxAh/SS1U6cONHmOpCEEJFIpI++AAAAlPSS1ezt7du8IhQKS0pK9NEXGBeGH8GI+Feu4ZY03YcRSAAA4A+9ZLWmpqb2L7af7m+CeHZSDYUaALCNXrJaQEBAm8QmlUpnzpypj74AwJTxbxASuslAI5Dm5uaEEKlUapjuAADANBkoq1VXV1MUZZi+wDAw/AgswbNyDRNGuknHd6Lx9PSkKKq1tXXYsGGqr7e2tg4ePJiu2EwWz06qAQCwkI6z2uXLl//73/+OHz9+165dvXr1Uv2TWCzWbV9gRCjUAICddJzVBAKBk5OTu7u7RCJp/0NsAAB94OXFjkE7erkX9v79+/XRLAAAADPD/Qo7Li7OYH2BXuGgGFiIT3NGMGGkO/RSqxFC6urqHj16pJz3WFdXd/To0fXr15vs1SAxVQQAwAD0ktVSUlI2bdqk+opIJBo5cqTJpjQ+QaEGrIWza0D0NAK5ZcuW9PT0srKyp59++vLly8XFxUOGDImNje10Qblc/ssvv5w4caKysrKrnRYUFFRXV2sVLwAA8ITus1pra6tAIPD396efymQyW1vbvXv3zp49m3nBioqKUaNG7dy58/jx42FhYQcPHtS807S0tOjo6LNnz2ofNwAAcJ/us5pIJJJKpXK5nBBiYWFRV1dHCBEIBCKRqL6+nmHBpKSkoKCgffv2JSUlpaamrlmz5tGjR5r0ePPmzZycHB8fH53Erw+8OamG4R1gOd7MGcGEEa3pZQTSx8dn2LBhtbW1o0aNGj9+fGlp6VdffVVXV2dtba1uEYqi8vLyxowZQz/19PR0dHQsLi7utC+pVPr+++8nJSX17NlTZx8AAAC4SS9Z7cCBA5MnT7ayslq5cqWLi8uUKVN27NgRHx8vEKjtrrGxUSaT+fj4ZGdnr1mzhhAiFotLS0s77WvdunUzZszAhUsAgMabcg20o6+Z/YmJifSD77//nqIohnxGk8lkhBCRSFRQUJCTk5OYmGhmZtbQ0MC81MmTJ+/fv7927dpO45FIJPSDsrKyzqOHjmD4EcAEKXeeXKGvrKaq05RGCLGwsCCE1NTUxMTEhIWFEUIoinJ0ZDrgqq6u3rRpU3Jycm1tLSGktbW1sbGxqampwyt1GTGZ8eakGgCYINWdJycynCGyGq2qqoohS1laWtrZ2ZWUlISGhtLDiUVFRdOmTWNosLKysl+/fqtXr6aflpeX//nnn48fP164cKFuIweCQg04hR8/XKMnjOCYuKsMlNXkcvno0aOvX7/O8J7w8PDU1NSRI0eam5unp6dbW1sHBQUp/5qQkJCTk6N61ODl5bV7927l07fffjssLIyu8wAAwDQZ7jqQnYqNjXVwcPD19fXx8dm3b19ycrImQ5cAAO1hzojJ0mWtdunSJUKIt7c3/UAV/fM1Zubm5l9++aW6v27cuHHjxo0Mi6vWbaBbPBjMAQATocus9s4777z44ov79u2Ljo6mZ38AhsUBAAxJl1ntwoULysenTp1SnYsol8s9PDx02BcYDAo14CgezBnBhBEt4MQVAADwh16y2okTJ9r/aAy3oQEAA8OcEROkl6xmb2/f5hWhUFhSUqKPvtiMB0MHXB/AAQBTgxFIAADgD13OFomMjGS4ciNFUUePHtVhd6BvKNSAB7g+ZwQTRrpKl7WaXC6X/eXGjRs1NTVSqVQqlT58+PDGjRs4rwYAAPqmy1otMzOTfrBs2bIVK1YEBAQo//Trr79u375dh32xHw6vAAAMT19zIFVTGiHE39//xo0bT5480Ud3oA+cHrQBUIWZkCZFL1mttbW1tbW1zYv19fX0TdQAAAD0RC9ZzcfHx9vb++OPP7527dq1a9d27Njh4+Nja2trY2Ojj+4AAJhxulyjJ4wYOwrO0MudaA4cOLBu3boffvjh6NGjCoXCxsZmxIgRDFcuBrbB8CMAcJS+7q+2cuXKlStX6qlx9sNUEQAAozDcr7Dj4uIM1hd0Bwo14CVOD0KC5vRVq9XV1T169IiiKOXTo0ePrl+/Hr9aAwAA/dFLVktJSdm0aZPqKyKRaOTIkUhpAABawBVGNKeXEcgtW7akp6eXlZU9/fTTly9fLi4uHjJkSGxsrD76YiFOf/kw/Ag8hkFIU6D7rNba2ioQCPz9/emnMpnM1tZ27969s2fP1nlfAAAAqnSf1UQikVQqlcvlhBALC4u6ujpCiEAgEIlE9fX1Ou8OdAiFGvAeyjXe09evsIcNG1ZbWztq1Kjx48eXlpZ+9dVXdXV11tbW+ugOAACAppesduDAgcmTJ1tZWa1cudLFxWXKlCk7duyIj48XCPh/OzdOn1QDANbCFUY0pK+Z/YmJifSD77//nqIoU8hnXIfhRzARXL/jGjDTS7J59OiR6ik0OqUVFhbqoy8AAAAl3ddqPj4+9B2xg4ODk5OT6ReXLFny008/Xb9+nXlZuVyen5/f0tLi5ubm5OSkSXdSqfTXX39taWlxdnYWi8XdDB4AADhNx1ltwYIFFhYWR48ebWxsnDBhwq+//tqjR4+oqCihULh//37mZSsqKiIjIwcMGNCvX7/ly5fHxcVNnz6deZGSkpK5c+e6u7v37t27oKDgtddeW7dune4+jQnBgAyYFAxC8piOs1pJScmePXscHR0JIZs2bYqKiiKEREZGKk+zMUhKSgoKClq7di3dTkREREhISN++fRkWWbVqVURExPvvv08IuXXr1uuvvx4dHT1o0CDdfJiuw1QRAADj0vF5tebmZktLS/qxu7s7IeTUqVOapDSKovLy8saMGUM/9fT0dHR0LC4uZl5q/fr10dHR9ONnnnmGEIIbk2oBB61ggrj4wzVMg9SEfqcmOjk5aXh6rLGxUSaT+fj4ZGdnr1mzhhAiFotLS0uZl5JIJH369Ll27dqJEyfmz58/depUiUSig7gBAICbdD9bZOnSpcrHlZWV06ZNox8LBILMzEx1S9E1lkgkKigoyMnJSUxMNDMzo2eddOrbb789c+bMs88+GxYW1r3YAQCA23Sc1fr16/e///1P+dTBwUH5VCgUMixoYWFBCKmpqYmJiaGTE0VR9Pm5TtF32b506VJUVNSePXu8vLzav0dZw5WVlWn0SbqOoyfVMPwIJgtzRjTBuQEwHWe1H374QbsFLS0t7ezsSkpKQkND6Qn6RUVFyjqvQxRFffbZZ1FRUc899xwhxNvb28/Pr6CgoMOspr9kBgDAY6o7T05kOBZd8iM8PDw1NVUqlRJC0tPTra2tg4KClH9NSEhos0IFAkFhYeHXX39N35u0tLS0oKDgpZdeMnDYnIYDVQBuwYSRTunrillaiI2NTUhI8PX1FYlEdnZ2ycnJnV5n6+uvv05ISPD09LS0tJRKpYsWLQoMDDRMtADAAxiE5B8WZTVzc3P6DFmHNm7cuHHjxjYvOjs7Hzp0SM9xAQAAZ7BoBJLTuDhVBIeoAISbP1wDBshqAADAH8hqAABcggkjzJDVTBSGHwGUMAjJJ8hqOsDFk2oAALyErGaKUKgBtIFyjTeQ1QAAgD+Q1QAAOAYTRhggq3UX506qYfgRoEMYhOQHZDUAAOAPZDXTgkINAPgNWQ0A4P9gEJIHkNUAALgHE0bUQVbrFm5NFcHwI0CnUK5xHbIaAADwB7KaqUChBgCmAFkNAOBvMAjJachq2uPWSTUA4BlMGOkQsppJwPAjAJgIZDUAgLYwCMldyGoAAMAfyGpa4tBJNQw/AmgB5RpHIasBAHAVJoy0h6zGcyjUAMCkIKtpiSvDjwCgNQxCclEPYwfwN3K5PD8/v6Wlxc3NzcnJSZNFpFJpQUGBTCZzdnYWi8X6jhAAANiMRbVaRUXFqFGjdu7cefz48bCwsIMHD3a6yM2bN4ODg3fv3p2bmztz5sxPPvnEAHFyCIYfAcDUsKhWS0pKCgoKWrt2LSGkpKQkIiIiJCSkb9++DIusX78+ODiYXuSPP/4YO3bs7NmzUbEBgK7Qg5BsPjqkJ4zgnIgSW2o1iqLy8vLGjBlDP/X09HR0dCwuLmZeaunSpYsXL6YfDxgwgBDy+PFjvcbJISzfFAEA9IEtWa2xsVEmk/n4+GRnZ69Zs4YQIhaLS0tLmZfy9PS0t7enH2/dutXFxeXll1/We6wAYEowZ4Rb2DICKZPJCCEikaigoCAnJycxMdHMzKyhoUHDxXfs2JGZmXno0CF1b5BIJPSDsrKy7kfLfijUAEAnlDtPrmBLVrOwsCCE1NTUxMTEhIWFEUIoinJ07Pz4SC6Xf/zxx2VlZVlZWQzvN5FkBgCgW6o7T05kOLaMQFpaWtrZ2ZWUlIjF4oCAAEJIUVHRoEGDmJeSy+Xz58+vr6/PzMzUJAUCAGiB5YOQuMKIKrZkNUJIeHh4amqqVColhKSnp1tbWwcFBSn/mpCQ0P4wYc2aNY2NjcnJyZaWlgaNld0w/AgAJostI5CEkNjY2ISEBF9fX5FIZGdnl5ycLBB0knQzMzPJ34viDRs20AOYAABggswUCoWxY9A7iURiOufVUKgB6AmbNy7D/GSNE/tSFo1AAgAAdBOyGq+w+VgSgOvYPGcEE0aUkNUAAIA/kNUAAIA/kNX4A8OPAPrG5kFIoCGrAQAAfyCr8QQKNQDDYG25hgkjNGQ1AADgD2Q1PkChBmBIrC3XgCCrAQAAnyCrAQB0Gco11kJW4zwMPwIADRNGCLIaAIB2UK6xE7Iat6FQAwBQhawGAKAllGsshKzGYSjUAADaQFYDAOAPTBhBVgMA0B4GIdkGWY2rMPwIANAeshoAQLegXGOVHsYOALRhyEJNIpEYpiMAwygrKzN2CKBHyGrQOewFgDf0dJRGl2ssOSlATxhRfBFs7ECMAyOQ3MOejQcAgG2Q1QAAdIBVZ9dMtlAjbBuBlMvl+fn5LS0tbm5uTk5Omi94/vz5gQMH9u3bV3+xsQQKNZ0rLCxsbm4OCgqin1ZUVPz888+EEGdn58DAQOZl8/LyJBLJc8891+b1LjXSfQbuThOFhYUymeyVV15RfUV1PWvXZjdbAFPAoqxWUVERGRk5YMCAfv36LV++PC4ubvr06Z0uRVHUzp07N2/evHXr1tDQUAPECezxySefODk5zZ8/vzuNXLhwoba2VrmvpCiqpaXl9OnTLS0tnWaIDRs2LFq0qH1W61IjHaqtrf3mm29KS0spiho+fPh7770nEKgdWel+d+osXLiwtbWVEGJubh4YGDh16lSGMFTt3bu3vr5eNau1Wc8Mzpw5Qwhp/0E0b8FYWHV2zWSxKKslJSUFBQWtXbuWEFJSUhIRERESEsJcfsXFxZ07d27cuHHW1taGChPYory8/MCBA9XV1XPmzOnZs6fW7SxcuFD16fPPPz937lxCSF5entZtdr+RCRMmuLi4vPXWWzKZbNWqVXfu3Nm8ebP+ulMnLS1t6dKl7u7ujY2N69ev379//5EjR7Rrqs16ZpCRkTF8+PD2WU3zFsCUseW8GkVReXl5Y8aMoZ96eno6OjoWFxczLzVu3Ljc3NyVK1fqP0BWwGGgqvT09BkzZgQHB2dkZNCvZGRk+Pn5Kd9QXV3t4OBQWlp69erVV155xd7e3t7efvXq1co3HDx4kH4xKiqq0+7UNXL9+nUvLy9bW9vAwMDq6mqGFqqrq9988007Ozt7e/v33ntPJpOpe6dMJisoKNi2bdvYsWMnTJiQlpbm4+ND/+n+/ftvvPEG3Uh0dHRDQ0NXY/7++++jo6PPnDkzaNAgCwuLmJgY5g8eEhLy1ltvzZ8//7vvvjt69Cgddoct37hxY+jQoQ4ODhMmTHjw4IGyhQ7Xs7r1GRERkZaWtmjRIvpPjx49UteCurURGBiYmprq5+dna2vr5+d3//595g8IfKNgh7q6OldX18bGxsOHD69evVqhUCxYsOCLL77QcHFvb++ffvpJ3V9dXV11E6WxlYU/a/hO2bn25HJ5//79r127duDAgeDgYPrFJ0+eWFtbFxQU0E//+c9/BgQEKBSKiRMn0t+lmpoaS0vLoqIi1ab27NkzZcqUNu2npKTMmDFD9ZUOGwkJCQkICHjy5IlCoZg8efKiRYsYGhk/fvwHH3xABz9+/PjExESGD+ji4vL222/fvXu3zeshISHx8fH043Hjxi1btqyrMZ86dapPnz7BwcG//fabVCptbGxkCMPc3PzEiRP041OnTllaWsrlcnUtDxs27LPPPlMoFA8ePBgwYMDs2bNVm2qznhn+U6ZMmbJnz572wbT/n+pwbYSEhIwYMaK+vl6hULz66qvKN9AM8H02ynZqGOzcG7TBlhFI+gBQJBIVFBTk5OQkJiaamZkxH4eaGqMXasa9ZGqbOV15eXl9+vRxc3NzdXWNjo6urKx0cnKysLCYPXt2Wlra8OHDCSEpKSnx8fGEkCNHjlAUdfv27crKypdeeqmystLX17erAahrJCoqysLCghAye/bsjz76SN3iLS0tP/zwQ2pqaktLCyFkzpw5a9asUa1R2vj5558TExNffPFFBweH8PDwDz/8sG/fvi0tLXl5efv27aPfM2/evCVLljCMTHYYs5eXV21t7apVqzw8PAghnQ7ebtiwYe/evTKZLC8vLzU1lT6v1r5lb2/voqKi7777jhBib28fEhLCUIyqi405kjYY1sbixYt79epFCAkPDz937lyXmgWuY0tWo/cLNTU1MTExYWFhhBCKohwddTZNVvnTS/ygWGusmiuckpLi4uJy8OBBQsiQIUP27Nnz8ccfE0LmzZsXEBCwbdu2Cxcu3L9//6233iKEfPrpp1u3bg0MDLS0tLx37552Papr5JlnnqEfWFhY/O9//1O3eH19PSFk1qxZylcGDBjA0N1zzz23a9euXbt2FRYWfvrpp0FBQVevXqVHOB0cHOj39OzZk3l4rcOYRSIRIUR1HgezESNGuLi4zJ07t6ioyMvLS13Lzc3NqrFZWlrSH7lLsXUJw9qgPyMdBkVRWjTeHTybM8K5qwuxJatZWlra2dmVlJSEhoaKxWJCSFFR0bRp03TVPteTGZ82ku6rq6vLzs6OioqiJ0fY29vv3buXzmpeXl5ubm4ZGRn5+flz584VCATV1dUrV668deuWi4sLIYQuULqKoRGpVEo/aGlpYZjcZGNjQwg5cOCAvb19p93dv3//7t27dO3i5+eXmZlpbW3d0NBAt19XV2dra0sIkclkDK0xf3ANpzISQl555ZWQkJA7d+4sWLCArns6bJmu+RobG+kHT548YWhTJ/8pXVoboDXVnScnMhxbZosQQsLDw1NTU+l9RHp6urW1teoU3oSEBE6sUDCAPXv2+Pn57fxLdnZ2VVVVYWEh/dd58+b98MMPBw8enDdvHiGEHvGjk0pubm51dTU9Vb1LGBqh60VCyE8//RQSEqKuhZ49ewYEBGzfvp1+mpGR8c0336h7c3l5+ahRo27cuKFs2dra2srKysLCwtvbOy0tjX49Kytr6tSpWsSshRUrVjx+/DgpKUldyz179hSLxd9//z0hpLm5+ezZswytMccmEAiqqjo/huvS2jAwVv0i29SwpVYjhMTGxiYkJPj6+opEIjs7u+Tk5E4PJ/39/elzb62trUuXLjUzMxs9evSXX35pkHjBaPbu3RsdHa182qNHj+nTp+/du5eeADl79uz333+fHjcjhDg5Oc2YMWPYsGEDBgx49dVX582bt2LFCkdHR19f36effpoQIpPJZDIZfbx/9epVum5QffHhw4fqGnn06NGLL744evRomUx2//79kydPEkIePXo0cODA9o1kZmZOnjz5p59+oihKKpUePnxY3QcMDAz8/PPPAwMDHRwc6uvrzc3Nv//+e3pzyMzMfPPNN48ePVpbW+vo6Pj111+r605dzEOGDNFinQsEgtTU1FGjRk2aNMnV1bXDljdv3jxjxgw6tqlTp969e5cQ0tLS0n49V1VVddgCPS46a9asmTNnbtmy5cmTJzt27JgyZUqHLfTq1av92tDiowHPmCkUCmPHoHcSiYTTI5DGHX7k+toDUGXI7zP/ThxwYm/AohFIAACAbkJWYzv+He4BmAicXTMKZDUAAOAPZDVWQ6EGwGko1wyPRXMgAfQEN2rhKxb+z4LRIasBJ+FGLRy6UYtyLfXt2zc4OFh5EfPu09//LHAXRiDZC8OPDCZMmHDjxo233norMjJyz549cXFxDG+mb9QycuRInYeRlpbWt2/fkSNHenh4rF+/nr7Ym3YWLly4YsUKTd6ZkZFx586d7rRgYMq11K9fvwULFrz33nu6all//7M6hEFIA0NWA+7BjVoMdqMWdeuzq3d7odfS0qVLDx8+vH37dplMpu4Ddtiy5jfxAUBWYykUagx69Ojh4uKyZMmSiooKQoinp2dkZCT9p1mzZr3wwgs1NTXV1dX3799PTExkaGf58uXh4eHV1dXl5eUbNmyg7+fXp0+fnJycVatW5eTk1NXVaX6pmoqKCktLS3oEssOWo6Kipk6dev/+ffqaxcoFp0+fXl1dvXnzZtXLAXfYAiEkIyPj9ddf37ZtW3V1dXV1NX0txA5bULc2LCwsdu/effLkybq6Oisrqy+++ILhQ6lbn11qRJVyhFbdB+yw5ejoaGdn55qamvv379+5c4e+tzCHoFwzJGQ14CR6jsCLL744aNCghIQEumShb03ywQcf0O+ZN29eVlYWQyNHjhxZunTp7du3r169St8MhRCieqOWnj17WllZMUeyYcOGqKioyMjIGTNmqN6opU3LMpmsqKiIvmY/faMW5mY7jK1LGNaG6o1aGFpmXp8aNkK7cOFCXl5ebm7u7Nmzo6KievTowfAB27RM38QnPj6+paVFJpPNmTMnJyenq2sDTAdmi7AROws14x5stlkhuFFLp7p/oxbm9dmlu73k5uYWFhYKBIK3336bPq/G8AHbtNzVm/iwE89uT8NmyGqgKfZskLhRiya6f6MWHd7q5R//+IdqedqlD9ilm/gAYAQSuAc3ajHMjVr0d6uXLq38Lt3Eh81wdk85U6wAABD2SURBVM0wUKuxDoYpOoUbtRjsRi16utWLupWvbuC3w5v4qPufpathMFm4Ew3rsC2rcWvtATAz+veZbRt4lxh97WkCI5DswulvPACA0SGrAQAAfyCrsQgKNQDew5wRfUNWYwt80QEAug9zII2Pzmeo0gBMBH6RrVfIasbElXwmkUiMHQIAgEaQ1YyDK/mMEML+ibwAnINyTX+Q1QyNQ/kMAIBzkNUMB/kMAJRQrukJspreKSc34usLAKBv3M5qcrk8Pz+/paXFzc3NycnJ2OG0heIMABigXNMHDme1ioqKyMjIAQMG9OvXb/ny5XFxcdOnTzd2UP8H+QwAwCg4/CvspKSkoKCgffv2JSUlpaamrlmzhr4hsnGVv+lIH3xpkdI4NIGeK6FyJU7CnVC5EifhSKi41IjOcbVWoygqLy9PecslT09PR0fH4uLisWPHGisk1GcAoB3VxIZ9SDdxNas1NjbKZDIfH5/s7OySkpLExESxWFxaWmqUrIZ8BgBam/ibjeqvQhlKN+xkNMHVrCaTyQghIpGooKAgJycnMTHRzMysoaHBwGEgnwGAbjHsT5jHKrEjonH1rqFPnjzx8vI6ffp0XV1ddXV1QEDAggULfHx83nnnnfZv1sfw+tGX6gkhE3+z0XnLAABaoHdKNP3tmth/sSGu1mqWlpZ2dnYlJSWhoaFisZgQUlRUNG3atA7frL//Brb/9wKASTLlXROH50CGh4enpqZKpVJCSHp6urW1dVBQkLGDAgAAY+LqCCQhRCqVJiQk/Oc//xGJRHZ2dlu2bHF3dzd2UAAAYEwczmoAAABtcHgEEgAAoA1kNQAA4A9kNQAA4A/hqlWrjB2DpuRy+ZkzZ27evGlhYWFra9v+DQUFBRYWFtbW1lo31WkX3YyztLT08uXLjY2NDg4O3WlKJ3EytNPU1HT69Olbt24JBII+ffqwIVRCyPnz583Nza2srLrTuAHi7DBUqVR65syZ33//naKovn37siTU9nEqsWRrYg6VbRtUh3GycGtS921k7QbVJZzJahUVFZMmTbp169b9+/c3bNjQu3dvDw8P1TekpaXFx8dLJJLBgwdr11SnXXQzzri4uN27dzc0NOzcufP27dvBwcFGjJOhnYsXL06bNq2lpeXu3btfffVVZWXlyJEjjRsqRVHffvttfHy8t7f3oEGDmDs1YpzqQr158+aUKVPu3r179+7dLVu2VFRUGP1/v8M4lViyNTGHyrYNqsM4Wbg1qfs2snOD0oaCIxYvXrxy5Ur68ZUrV9zc3B4+fKj8a3l5+aRJkyIiIg4fPtxmwUuXLuXm5mrSFHMX3YzzyJEjISEhjY2NCoXiwYMHCxcurKurM2KcDO1ERkZu27aNfr2ystLV1fX27dtGDPWDDz4ICAhYu3att7f3Tz/91GmnxoqTIdTo6Ghl+7dv33Z1dS0vLzdiqOripLFna2IIlW0blLo42bY1KdR/G1m4QWmHG1lNLpe7ubmdPn1a+UpISMixY8fox83NzRMmTCgvL4+Kimq/HcbHx7u6unbaFHMX3Y9z/vz5qampDMsaMk7mUGfOnLl37176xbq6OldX199//92Ioebl5dXU1CgUCtX9hSaNGzhOdaEqFIorV648ePCAfiyTyVxdXYuLi40Yqro4FWzamphDZdsGpS5Otm1NCjXfRnZuUNrhxmwR1Sv0r1mzhhBCX6Gf/uu6detmzJhBXzerPR8fn8mTJ3faFHMX3Y+zsLCwtbU1LCzMy8srODj4xx9/NGKczKHGxsbu2rUrIyPj+PHjsbGxs2bNajNCZeBQR48e3f5shCaNGzhOdaESQjw9Pe3t7enHW7dudXFxefnll40Yqro4CZu2JuZQ2bZBqYuTbVsTUfNtZOcGpR1uZDXVK/Tv37+fEKK8Qv/Jkyfv378fGRmpbtnp06dv3Lix06YYutBJnC0tLbm5uTt27Lh8+fKKFSuWLVt269YtY8XJHKq9vX3//v3PnTt38uTJ1tbWAQMGtFnWwKF2NX5WxdnGjh07MjMzv/322zavsyRUVm1NzNi2QanD5q1J9dvI0Q2qQ9y4urGFhQUhpKamJiYmJiwsjBBCUZSjo2N1dfWmTZuSk5Nra2sJIa2trY2NjU1NTR3O6WJuSt3rOomTENKzZ8+5c+fSM7VGjx7t7u5+7ty59mfpDRMnc6jz5s2bM2cOvWurq6t79dVXJRKJv7+/sULtavysilNJLpd//PHHZWVlWVlZnTZulFDZtjUxY9sGpQ47t6b230bObVAMuFGrKa/QLxaLAwICCCFFRUWDBg2qrKzs16/f6tWrly1btmzZsvLy8gMHDuzevVuLptS9rpM4CSE+Pj7V1dWqbxaJRMaKk6H91tbWe/fuKRu0tbV1dXW9d++eEUPtUqdsi5Mml8vnz59fX1+fmZmpyeZtlFDZtjUxY9sG1SF2bk0dfhu5tUEx40ZWI2qu0O/l5bVbhbu7e0xMzMKFC1UXTEhIaHN/NXUX+9fJTQDUNTJp0qTdu3dXVVURQgoLC69duzZs2DAjxqmuHZFINGDAgDNnztDvuXXr1m+//ebs7GzcUDWPn4VxEkLWrFnT2NiYnJxsaWnZ4RvYECoLtyYGLNyg2mPn1qTu28ihDaoTBpuX0k3Nzc2LFy9+6aWXhg4dGhIScvXq1fbv0WTWFkNTmnTRnTi/+uqrl1566eWXX/bz8zN6nAzt3LhxY9KkSSEhIZGRkX5+fjt37jRuqH5+fu7u7u7u7q6urm5ubu7u7osXL9akccOvUnWhurbT5gvAklWqig1bE3OorNqg1MXJtq1Jof7byMINSjscu2Z/a2urTCZTd8Crk6Z00oW6RiiKam5uZjhRYeA4ddu+vkPtUqdaLKLXOLXDlVCNFScLN6gudarFIvpepRwKVR2OZTUAAAAGnDmvBgAA0ClkNQAA4A9kNQAA4A9kNQAA4A9kNQAA4A9kNQAA4A9uXAcSwDRVVFRs3rz58uXLLS0tbm5uO3fuNHZErHPz5k360ll9+/Ztc+ULnZDL5fn5+fT6d3Jyav+G8+fPDxw4UMP7m6sjlUoLCgpkMpmzs7PqDROYe++w66ampnPnzlEU9cILLwwcOLA7UXEUZ+6FDaBDixYtKikpCQwMpJ+uX78+MzNz/Pjxxo2qvVmzZslksg8++CA0NNTd3V2TndSiRYvWrl2bkpJy6tSp69evDxw4sHfv3gYI1Vi++uqrnTt3ZmVl/e9//9P5/yDzPZ2Z7yeuOS1uTq2uay1uvc0/qNXAFDU2NjY2Nqp7SmttbWW+YK46FEUpFAqhUKj5Ih32RVFUeXn59u3bu3QNvcbGRjc3t6ioqIaGhqysrMmTJ+/evdvT01P1PXK5vEvhsdnq1atXr169cuVK+lYDhJCLFy+am5u7u7sTQiiKOnbsmL+/P13QnD9/3tHR8c8//8zLy7Ozsxs/fry6+8zRkpKSgoKC1q5dSwgpKSmJiIgICQmhm4qLizt37ty4ceOsra27+RHWr18fHBxM9/LHH3+MHTt29uzZYrFYXe8MXW/evHn27Nn01TurqqpGjRoVGRlpahUbzqsBtLV582YvLy8/Pz8PD4/169fL5XL69U8++eSjjz5Svk31aXV19dChQ6uqqtatW+fu7u7m5hYXF9edvvz9/ek7iy5ZsmTo0KFDhw4dO3ashvE7OjoGBQWNHz8+LS3Nx8dHOR5TX18fFxfn4eExZMgQX1/fgwcP0q+fOXPG19dXmRUIIbW1tb6+vsrL8nJLampqVlYW/Vgul7///vt//PEH/TQtLW3Lli1bt251cnI6cuRIdHQ0QzsUReXl5Y0ZM4Z+6unp6ejoWFxcTD8dN25cbm7uypUrux/w0qVLFy9eTD+mb8D2+PFjht4ZulYoFLa2tvRjGxsb+lN0P0JuQa0G8Dc7d+7817/+lZ2d7ezsXFlZOW3atF69esXGxhJCpFJpa2ur8p2qT+3t7VtaWj799NOWlpYDBw7079+/pqamO32dOnVKKpX6+/tv2rRJOVKqhTfffHPx4sV1dXW2trY7duy4cuVKQUGBra3t8ePHFy9ePGzYsIEDBwYGBtrY2GRlZb3zzjv0UkeOHLGxselOv+xkZmZ27969AwcOEEL8/f2Dg4Pr6+vpvX97qvd0LikpSUxMpO/pTB9ejB49WldRqVbSyptT19fXq+udoevY2NgVK1YIhUI7O7uMjIz2t942BajVwEQVFBQs+ktBQYHy9czMzIiICPp2IU5OTjNmzMjOztakQRsbG3rA0Nvb297eXpOZCwx9WVlZ9erVixBiYWFh9Zcuf0hCnnrqKULI1atXCSFLlizJysqij+VDQ0MJIbdv36bfNnPmzIyMDOVS3333XUREhBbdsd/gwYPpB8888wwhpKysTN07DX9P567enLq9Tm+9bQpQq4GJcnR0pPfshJDHjx8rX793756rq6vyqVgspm/i1akePXoEBwd36XyV1n1pjr58uUAgIIQ0Nzfv3r37+vXrUqmUHphSDk/NnDlz69atBQUFAQEBly5dun379vTp03UbCecY8p7OOrk5Nen6rbd5CVkNTJSzszO9syCEnD9/nvkOxR1qf78L1TnZLEEnbG9vb0LI+++/X1VV9cknn3h6epqZmXl5eSnfZmVlNWPGjN27dwcEBGRlZb3xxhvK0zOs9csvvzQ0NNDzHtXNf/nzzz+1bl95T+fQ0FD6f7aoqGjatGlaN6gOfXPqHj16ZGZmKu/bokXvWtx6m5cwAgnwN46OjspxOULI7du3VQ+QVc+9q1Z4+uhLJ44cOeLu7m5ubk5R1OnTp2NjY/39/a2srB4+fNjmnZGRkWfOnLl161Z2djYnhh8vXLiwefPmurq6pqamS5cuKQ8prK2tlbvyvXv3dqcL7e7pXFJSIpFINP9xodY3p25Dk1tvmwJkNYC/mTp1akZGBv3D3kePHmVmZoaHh9N/6t+//6VLl5qamgghhYWFRUVF+uurO2QyWVNT08WLF//xj3+cOnVq+fLlhBCBQGBtbX3z5k1CSH19/VdffSUSiZ48eaJc6rnnngsMDFy8eLGHh4ebm1v3w9C3mJiY559/fvjw4b6+vo6OjnPnzlW+fu3atVGjRo0dO/aFF14QiUTKmaVdFRsb6+Dg4Ovr6+Pjs2/fvuTkZHoslxDi7+/v4eHh4eHR2Ni4dOlSDw+PJUuW0H86f/58r1696GFATWRmZl64cEGigj69qq53hq63bt2an58fGho6a9asyMjIZcuW0TNpTQpGIAH+Zv78+Q8fPhw1apS9vf2jR4+mTp1K//qHEBIREfHvf/972LBhlpaWAQEBkydPVp0Nr9u+uuPw4cOHDx82Nzd/+eWXDxw4QA8/EkI+/PDDVatW7dmzx8bGJikpiRCyatWqBw8eKKc+zps3LzIycsOGDd2PwQBsbGzS0tLoaaiqv/YTi8Vnz55tamqysLAQCASTJk1S/mnbtm3KxyKRiGGqCM3c3PzLL7/s8J7Ov/76q7qliouLo6KiNJ/doy4Mdb0zdC2RSHJycth2q3QDw72wATpAUVRzczO9W2zzpydPnvTo0UO7H2h3tS+doyhKKpUq93cURal2+ssvv3z00Udnz541QCR8RVHU0KFDT5482c1raIHWkNUAgBBCqqur33rrrfDw8JiYGGPHwmF1dXVXrlzh30/9OARZDQDI4sWLT5w4MXHixI0bNxo7FoBuQVYDAELPp+DNxSHBlCGrAQAAf+CcMAAA8AeyGgAA8AeyGgAA8AeyGgAA8AeyGgAA8AeyGgAA8Mf/A8pMuOB8ShSpAAAAAElFTkSuQmCC"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "% Calculate Power Generation Over Time\n",
    "isFixed = 1;\n",
    "date = datetime(localYear,localMonth,localDay,'TimeZone',TZ);\n",
    "[times, sRad, pRad] = hourlyPanelRadiation(date, lambda, phi, UTCoff, tau, beta, isFixed) ;\n",
    "plot(times,sRad,times,pRad)\n",
    "title(['Solar and Panel Radiation for ' datestr(date,'mmmm dd yyyy')])\n",
    "xlabel('Hour of Day');\n",
    "ylabel('Radiation, kW/m^2')\n",
    "legend('Available Solar Radiation','Solar Radiation on Panel', 'Location','South')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "** To view the code for this example, go to the* ***View*** *tab and click* ***Output on Right*** *or* ***Output Inline****.*\n",
    "\n",
    "\n",
    "*Copyright 2019 The MathWorks, Inc.*\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "MATLAB Kernel",
   "language": "matlab",
   "name": "jupyter_matlab_kernel"
  },
  "language_info": {
   "file_extension": ".m",
   "mimetype": "text/x-matlab",
   "name": "matlab"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}