1.7_Irradiância_em_um_plano_inclinado.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jHNbxFTgNXc-"
   },
   "source": [
    "# Transpondo a irradiância global (GHI) de céu claro para o plano inclinado dos módulos (POA)\n",
    "\n",
    "Adaptado do link: https://pvlib-python.readthedocs.io/en/stable/auto_examples/plot_ghi_transposition.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BY5eJ_2SNXdA"
   },
   "source": [
    "Este exemplo mostra como usar o método\n",
    ":py:meth:`pvlib.location.Location.get_clearsky` para gerar dados de irradiância global (GHI) de céu claro assim como como se usa a função\n",
    ":py:meth:`pvlib.irradiance.get_total_irradiance` para transpor para o plano inclinado dos módulos (POA).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iEmjib_Hbtti"
   },
   "outputs": [],
   "source": [
    "try:\n",
    "  import pvlib\n",
    "except ImportError:\n",
    "  !pip install pvlib\n",
    "  import pvlib\n",
    "\n",
    "from pvlib import location\n",
    "from pvlib import irradiance\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# For this example, we will be using Curitiba\n",
    "tz = 'America/Sao_Paulo'\n",
    "lat, lon = -25.4284, -49.2733\n",
    "\n",
    "# Create location object to store lat, lon, timezone\n",
    "site = location.Location(lat, lon, tz=tz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J0QFHhBRbxnQ"
   },
   "source": [
    "## Criando Funções"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w8L_dDbfNXdH"
   },
   "outputs": [],
   "source": [
    "# Calculate clear-sky GHI and transpose to plane of array\n",
    "# Define a function so that we can re-use the sequence of operations with\n",
    "# different locations\n",
    "def get_irradiance(site_location, date, tilt, surface_azimuth):\n",
    "    # Creates one day's worth of 10 min intervals\n",
    "    times = pd.date_range(date, freq='10min', periods=6*24,\n",
    "                          tz=site_location.tz)\n",
    "    # Generate clearsky data using the Ineichen model, which is the default\n",
    "    # The get_clearsky method returns a dataframe with values for GHI, DNI,\n",
    "    # and DHI\n",
    "    clearsky = site_location.get_clearsky(times)\n",
    "    # Get solar azimuth and zenith to pass to the transposition function\n",
    "    solar_position = site_location.get_solarposition(times=times)\n",
    "    # Use the get_total_irradiance function to transpose the GHI to POA\n",
    "    POA_irradiance = irradiance.get_total_irradiance(\n",
    "        surface_tilt=tilt,\n",
    "        surface_azimuth=surface_azimuth,\n",
    "        dni=clearsky['dni'],\n",
    "        ghi=clearsky['ghi'],\n",
    "        dhi=clearsky['dhi'],\n",
    "        solar_zenith=solar_position['apparent_zenith'],\n",
    "        solar_azimuth=solar_position['azimuth'])\n",
    "    # Return DataFrame with only GHI and POA\n",
    "    return pd.DataFrame({'GHI': clearsky['ghi'],\n",
    "                         'POA': POA_irradiance['poa_global']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uec8Sag-b5sj"
   },
   "source": [
    "### Gerando gráficos usando a função definida acima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tables in c:\\programdata\\anaconda3\\lib\\site-packages (3.6.1)\n",
      "Requirement already satisfied: numpy>=1.9.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from tables) (1.18.5)\n",
      "Requirement already satisfied: numexpr>=2.6.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from tables) (2.7.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install tables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "colab_type": "code",
    "id": "4Rc5R0labmT1",
    "outputId": "a50d2cef-f620-4ecb-de99-4e179f125f0a"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get irradiance data for summer and winter solstice, assuming 10 degree tilt\n",
    "# in relation to latitude and a North facing array\n",
    "summer_irradiance = get_irradiance(site, '12-21-2020', abs(lat)-10, 0)\n",
    "winter_irradiance = get_irradiance(site, '06-21-2020', abs(lat)-10, 0)\n",
    "\n",
    "# Convert Dataframe Indexes to Hour:Minute format to make plotting easier\n",
    "summer_irradiance.index = summer_irradiance.index.strftime(\"%H:%M\")\n",
    "winter_irradiance.index = winter_irradiance.index.strftime(\"%H:%M\")\n",
    "\n",
    "# Plot GHI vs. POA for winter and summer\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n",
    "summer_irradiance['GHI'].plot(ax=ax1, label='GHI')\n",
    "summer_irradiance['POA'].plot(ax=ax1, label='POA')\n",
    "winter_irradiance['GHI'].plot(ax=ax2, label='GHI')\n",
    "winter_irradiance['POA'].plot(ax=ax2, label='POA')\n",
    "ax1.set_xlabel('Hora do Dia (Verão)')\n",
    "ax2.set_xlabel('Hora do Dia (Inverno)')\n",
    "ax1.set_ylabel('Irradiância ($W/m^2$)')\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vy1G1sP2NXdT"
   },
   "source": [
    "Observe que, no verão, não há muito ganho ao comparar a irradiância do POA com o GHI. No inverno, entretanto, a irradiância do POA é significativamente maior do que o GHI. Isso ocorre porque, no inverno, o sol está muito mais baixo no céu, portanto, um painel inclinado igual a (Latitude - 10°) terá um ângulo mais ideal em comparação com uma matriz plana. No verão, o sol fica muito mais alto no céu, e há muito pouco ganho para uma matriz inclinada em comparação com uma matriz plana.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qzCccy6Bcb8W"
   },
   "source": [
    "### Tarefa\n",
    "\n",
    "Encontre as inclinações (tilt) ótimas para Curitiba nos dias de solstício de verão e inverno."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q05IvPXYSLDb"
   },
   "source": [
    "# Desafio\n",
    "\n",
    "Reproduza a animação do site PVEducation, onde se varia a latitude e a inclinação do módulo. Como resultado, obtêm-se a irradiância global no plano horizontal, no plano inclinado (POA) e a incidente (simulando o seguimento perfeito do Sol em dois eixos): https://www.pveducation.org/es/fotovoltaica/2-propiedades-de-la-luz-del-sol/la-radiaci%C3%B3n-solar-sobre-una-superficie-inclinada"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xuj9ueOgaqC5"
   },
   "source": [
    "# Para saber mais\n",
    "\n",
    "https://github.com/brian-rose/ClimateModeling_courseware/blob/master/Lectures/Lecture15%20--%20Insolation.ipynb"
   ]
  }
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