04_spatial_variation_in_irradiance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial Variation in Irradiance\n",
    "\n",
    "This notebook looks a little deeper into a concept that was introduced earlier in [02_solar_power_forecasts.ipynb](02_solar_power_forecasts.ipynb): spatial variance in irradiance, and resulting fleet power, which is based on the maximum and minimum irradiance in each grid cell from a downsampled (\"coarsened\") version of the HRRR. We used a 10x10 coarsening window, which results in approximately 30x30 km grids. \n",
    "\n",
    "This value is normalized as: (max-min)/clear sky. This is our first indicator of forecast uncertainty. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load previous timeseries DataFrames:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_ac_hrrr_coarse_max_all = pd.read_pickle('example_data/p_ac_hrrr_coarse_max_all.pkl')\n",
    "p_ac_hrrr_coarse_min_all = pd.read_pickle('example_data/p_ac_hrrr_coarse_min_all.pkl')\n",
    "power_ac_cs_all = pd.read_pickle('example_data/power_ac_cs_all.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize to clear sky power:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_min_norm = (p_ac_hrrr_coarse_max_all-p_ac_hrrr_coarse_min_all)/power_ac_cs_all\n",
    "max_min_norm = max_min_norm.dropna() # drop rows with nan to filter to time range of HRRR data\n",
    "\n",
    "# At *very* low irradiance, sometimes HRRR reports higher than our clear sky model\n",
    "# For intervals where clear sky power is less than 0.1% of max clear sky power, set\n",
    "# normalized max-min power to zero\n",
    "min_cs_filter = power_ac_cs_all < power_ac_cs_all.max()*.001\n",
    "max_min_norm[min_cs_filter] = 0\n",
    "\n",
    "max_min_norm.rename(columns={'power_ac':'max_min_norm'},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(max_min_norm.index, max_min_norm.max_min_norm,drawstyle='steps-mid')\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.xlabel('Time, UTC (mm-dd HH)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's save the DataFrame for use later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_min_norm.to_pickle('example_data/max_min_norm_20210408.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will look at our second indicator of forecast uncertainty, which comes from the NOAA GEFS, an ensemble forecast model: [05_ensemble_variation_in_cloud_cover.ipynb](05_ensemble_variation_in_cloud_cover.ipynb)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}