oguna · July 20, 2020 22:49
diff --git a/solar-power-calc.ipynb b/solar-power-calc.ipynb
 {
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPc3cGDQh7Wma52AWDZY+tL",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/oguna/2299c42cc6acaba35f4fdec13b7e7440/untitled0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LzxU9AOUgCsT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import math\n",
        "import datetime\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uw8ELAg0gVJR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_jd(m: int, d: int) -> int:\n",
        "    md = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n",
        "    jd = 0.5 + d - 1\n",
        "    for i in range(m-1):\n",
        "        jd += md[i]\n",
        "    return jd\n",
        "\n",
        "def calcA1(jd: float, h: float, lat: float, lon: float) -> (float,float):\n",
        "    w = 2 * math.pi / 365\n",
        "    wj = w * jd\n",
        "    delta = 0.33281 - 22.984 * math.cos(wj) - 0.34990 * math.cos(2 * wj) - 0.13980 * math.cos(3*wj) + 3.7872 * math.sin(wj) + 0.03250 * math.sin(2*wj) + 0.07187 * math.sin(3*wj)\n",
        "    ee =  0.0072*math.cos(wj) - 0.0528*math.cos(2*wj) - 0.0012*math.cos(3*wj) - 0.1229*math.sin(wj) - 0.1565*math.sin(2*wj) - 0.0041*math.sin(3*wj)\n",
        "\n",
        "    Ts = h\n",
        "    T  = Ts + (lon - 135)/15. + ee\n",
        "    tt = 15*T - 180\n",
        "\n",
        "    phi = lat*math.pi/180\n",
        "    del_ = delta*math.pi/180\n",
        "    ttt = tt*math.pi/180\n",
        "\n",
        "    angH = math.asin(math.sin(phi)*math.sin(del_) + math.cos(phi)*math.cos(del_)*math.cos(ttt))\n",
        "    sinA = math.cos(del_)*math.sin(ttt)/math.cos(angH)\n",
        "    cosA = (math.sin(angH)*math.sin(phi) - math.sin(del_))/math.cos(angH)/math.cos(phi)\n",
        "    angA = math.atan2(sinA, cosA) + math.pi\n",
        "\n",
        "    return (angH, angA)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_DJI-FugZpj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lon = 135\n",
        "lat = 35.681236\n",
        "dt = datetime.datetime.now()\n",
        "jd = get_jd(7, 20)\n",
        "t = np.arange(4.0, 20.0, 0.1)\n",
        "xa = np.arange(len(t))\n",
        "ya = np.arange(len(t))\n",
        "for i in range(len(t)):\n",
        "    h,a = calcA1(jd, t[i], lat, lon)\n",
        "    ya[i] = h * 180 / math.pi\n",
        "    xa[i] = a * 180 / math.pi"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmEUhgBugk8N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "d02a622c-1e02-44b7-f4d5-7138843129d6"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(xa, ya)\n",
        "ax.set(xlabel=\"azimuth\",ylabel=\"dramatic\")\n",
        "ax.grid()\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0iM9zrH5SXzL",
        "colab_type": "text"
      },
      "source": [
        "- $H$ 水平面全天日射量\n",
        "- $Hb$ 水平面直達日射量\n",
        "- $Hd$ 水平面散乱日射量\n",
        "- $H0$ 大気外水平面日射量\n",
        "- $\\alpha$ 太陽高度角\n",
        "- $\\theta_a$ パネルの傾斜角\n",
        "- $\\theta_z$ 天頂角\n",
        "- $\\theta$ パネルへの入射角\n",
        "- $p$ アルベド（地表への反射率=0.2）"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "66lr6DEUifGd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 大気外水平面日射量[kW/m2]\n",
        "_, alpha = calcA1(jd, 12, lat, lon)\n",
        "wj = 2 * math.pi / 365 * (jd+0.5)\n",
        "rr = 1/math.sqrt(1.000110 + 0.034221*math.cos(wj) + 0.001280*math.sin(wj) + 0.000719*math.cos(2*wj) + 0.000077*math.sin(2*wj))\n",
        "h0 = 1.367 * rr * rr * math.sin(alpha)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nzR8gEfjWcBm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ec6788af-9356-45b9-8095-699da14c1303"
      },
      "source": [
        "print(h0)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.13329926077442636\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g1-n48-AXFeH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
 }
	{
	"nbformat": 4,
	"nbformat_minor": 0,
	"metadata": {
	"colab": {
	"name": "Untitled0.ipynb",
	"provenance": [],
	"collapsed_sections": [],
	"authorship_tag": "ABX9TyPc3cGDQh7Wma52AWDZY+tL",
	"include_colab_link": true
	},
	"kernelspec": {
	"name": "python3",
	"display_name": "Python 3"
	}
	},
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "view-in-github",
	"colab_type": "text"
	},
	"source": [
	"<a href=\"https://colab.research.google.com/gist/oguna/2299c42cc6acaba35f4fdec13b7e7440/untitled0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "LzxU9AOUgCsT",
	"colab_type": "code",
	"colab": {}
	},
	"source": [
	"import math\n",
	"import datetime\n",
	"import matplotlib\n",
	"import matplotlib.pyplot as plt\n",
	"import numpy as np\n",
	"import pandas as pd"
	],
	"execution_count": 3,
	"outputs": []
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "Uw8ELAg0gVJR",
	"colab_type": "code",
	"colab": {}
	},
	"source": [
	"def get_jd(m: int, d: int) -> int:\n",
	" md = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n",
	" jd = 0.5 + d - 1\n",
	" for i in range(m-1):\n",
	" jd += md[i]\n",
	" return jd\n",
	"\n",
	"def calcA1(jd: float, h: float, lat: float, lon: float) -> (float,float):\n",
	" w = 2 * math.pi / 365\n",
	" wj = w * jd\n",
	" delta = 0.33281 - 22.984 * math.cos(wj) - 0.34990 * math.cos(2 * wj) - 0.13980 * math.cos(3wj) + 3.7872 math.sin(wj) + 0.03250 * math.sin(2wj) + 0.07187 math.sin(3*wj)\n",
	" ee = 0.0072math.cos(wj) - 0.0528math.cos(2wj) - 0.0012math.cos(3wj) - 0.1229math.sin(wj) - 0.1565math.sin(2wj) - 0.0041math.sin(3wj)\n",
	"\n",
	" Ts = h\n",
	" T = Ts + (lon - 135)/15. + ee\n",
	" tt = 15*T - 180\n",
	"\n",
	" phi = lat*math.pi/180\n",
	" del_ = delta*math.pi/180\n",
	" ttt = tt*math.pi/180\n",
	"\n",
	" angH = math.asin(math.sin(phi)math.sin(del_) + math.cos(phi)math.cos(del_)*math.cos(ttt))\n",
	" sinA = math.cos(del_)*math.sin(ttt)/math.cos(angH)\n",
	" cosA = (math.sin(angH)*math.sin(phi) - math.sin(del_))/math.cos(angH)/math.cos(phi)\n",
	" angA = math.atan2(sinA, cosA) + math.pi\n",
	"\n",
	" return (angH, angA)"
	],
	"execution_count": 4,
	"outputs": []
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "D_DJI-FugZpj",
	"colab_type": "code",
	"colab": {}
	},
	"source": [
	"lon = 135\n",
	"lat = 35.681236\n",
	"dt = datetime.datetime.now()\n",
	"jd = get_jd(7, 20)\n",
	"t = np.arange(4.0, 20.0, 0.1)\n",
	"xa = np.arange(len(t))\n",
	"ya = np.arange(len(t))\n",
	"for i in range(len(t)):\n",
	" h,a = calcA1(jd, t[i], lat, lon)\n",
	" ya[i] = h * 180 / math.pi\n",
	" xa[i] = a * 180 / math.pi"
	],
	"execution_count": 7,
	"outputs": []
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "zmEUhgBugk8N",
	"colab_type": "code",
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 279
	},
	"outputId": "d02a622c-1e02-44b7-f4d5-7138843129d6"
	},
	"source": [
	"fig, ax = plt.subplots()\n",
	"ax.plot(xa, ya)\n",
	"ax.set(xlabel=\"azimuth\",ylabel=\"dramatic\")\n",
	"ax.grid()\n",
	"plt.show()"
	],
	"execution_count": 8,
	"outputs": [
	{
	"output_type": "display_data",
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"tags": [],
	"needs_background": "light"
	}
	}
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "0iM9zrH5SXzL",
	"colab_type": "text"
	},
	"source": [
	"- $H$ 水平面全天日射量\n",
	"- $Hb$ 水平面直達日射量\n",
	"- $Hd$ 水平面散乱日射量\n",
	"- $H0$ 大気外水平面日射量\n",
	"- $\\alpha$ 太陽高度角\n",
	"- $\\theta_a$ パネルの傾斜角\n",
	"- $\\theta_z$ 天頂角\n",
	"- $\\theta$ パネルへの入射角\n",
	"- $p$ アルベド（地表への反射率=0.2）"
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "66lr6DEUifGd",
	"colab_type": "code",
	"colab": {}
	},
	"source": [
	"# 大気外水平面日射量[kW/m2]\n",
	"_, alpha = calcA1(jd, 12, lat, lon)\n",
	"wj = 2 * math.pi / 365 * (jd+0.5)\n",
	"rr = 1/math.sqrt(1.000110 + 0.034221math.cos(wj) + 0.001280math.sin(wj) + 0.000719math.cos(2wj) + 0.000077math.sin(2wj))\n",
	"h0 = 1.367 * rr * rr * math.sin(alpha)"
	],
	"execution_count": 10,
	"outputs": []
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "nzR8gEfjWcBm",
	"colab_type": "code",
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 35
	},
	"outputId": "ec6788af-9356-45b9-8095-699da14c1303"
	},
	"source": [
	"print(h0)"
	],
	"execution_count": 11,
	"outputs": [
	{
	"output_type": "stream",
	"text": [
	"0.13329926077442636\n"
	],
	"name": "stdout"
	}
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "g1-n48-AXFeH",
	"colab_type": "code",
	"colab": {}
	},
	"source": [
	""
	],
	"execution_count": null,
	"outputs": []
	}
	]
	}
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