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March 8, 2013 21:17
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comparing scenario accuracy and spread
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| { | |
| "metadata": { | |
| "name": "scenario-metrics" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "from utils import *\n", | |
| "from scipy import stats\n", | |
| "from get_data import load_data\n", | |
| "from matplotlib import pyplot as plt\n", | |
| "\n", | |
| "powersystem = 'ERCOT'\n", | |
| "timezone='US/Central'\n", | |
| "analogue_hrs = 24\n", | |
| "obs_hrs = 36\n", | |
| "\n", | |
| "Ns = 5" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 1 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data, train, test = load_data(powersystem)\n", | |
| "test_days = pd.date_range(test.index[0].date(), test.index[-1].date(), freq='D')\n", | |
| "data" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 2, | |
| "text": [ | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "DatetimeIndex: 10873 entries, 2011-08-19 19:00:00-05:00 to 2012-11-14 18:00:00-06:00\n", | |
| "Freq: H\n", | |
| "Data columns:\n", | |
| "wind 10873 non-null values\n", | |
| "wind forecast 10873 non-null values\n", | |
| "wind capacity 10873 non-null values\n", | |
| "fcst_norm 10873 non-null values\n", | |
| "obs_norm 10873 non-null values\n", | |
| "dtypes: float64(5)" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 2 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "def get_metrics(scenarios_dir, test_days):\n", | |
| " metrics = DataFrame(columns=['accuracy', 'spread_over', 'spread_under'], index=test.index)\n", | |
| " for test_date in test_days:\n", | |
| " \n", | |
| " filenm = scenarios_dir + 'scenarios-{d}.csv'.format(d=test_date.date())\n", | |
| " times = pd.date_range(test_date, periods=analogue_hrs, freq='H', tz=timezone)\n", | |
| " obs = data.wind.ix[times]\n", | |
| " if 'analog' in scenarios_dir:\n", | |
| " drop_cols = ['probability', 'historical_time']\n", | |
| " else:\n", | |
| " drop_cols = ['probability']\n", | |
| " scenarios = pd.read_csv(filenm, index_col=0)\n", | |
| " prob = scenarios['probability']\n", | |
| " scenarios = scenarios.drop(drop_cols, axis=1).T.ix[:analogue_hrs]\n", | |
| " scenarios.index = times\n", | |
| " \n", | |
| " metrics.ix[times, 'accuracy'] = ((prob * scenarios).sum(axis=1) - obs)\n", | |
| " over_fcst = scenarios.min(axis=1) - obs\n", | |
| " over_fcst[over_fcst < 0] = None\n", | |
| " under_fcst = scenarios.max(axis=1) - obs\n", | |
| " under_fcst[under_fcst > 0] = None\n", | |
| " metrics.ix[times, 'spread_over'] = over_fcst\n", | |
| " metrics.ix[times, 'spread_under'] = under_fcst\n", | |
| " return metrics" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 100 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "metrics_an = get_metrics('analogs/{p}/Ns{n}/'.format(p=powersystem, n=Ns), test_days)\n", | |
| "metrics_an" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 101, | |
| "text": [ | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "DatetimeIndex: 6575 entries, 2012-01-01 00:00:00-06:00 to 2012-09-30 23:00:00-05:00\n", | |
| "Freq: H\n", | |
| "Data columns:\n", | |
| "accuracy 6575 non-null values\n", | |
| "spread_over 1083 non-null values\n", | |
| "spread_under 1207 non-null values\n", | |
| "dtypes: object(3)" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 101 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "metrics_mm = get_metrics('moment_matching/{p}/Ns{n}/'.format(p=powersystem, n=Ns), test_days)\n", | |
| "metrics_mm" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 102, | |
| "text": [ | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "DatetimeIndex: 6575 entries, 2012-01-01 00:00:00-06:00 to 2012-09-30 23:00:00-05:00\n", | |
| "Freq: H\n", | |
| "Data columns:\n", | |
| "accuracy 6575 non-null values\n", | |
| "spread_over 1144 non-null values\n", | |
| "spread_under 741 non-null values\n", | |
| "dtypes: object(3)" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 102 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "metrics_ostd = get_metrics('one-stdev/{p}/'.format(p=powersystem), test_days)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 103 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Compare" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# hist of scenario mean - observed wind\n", | |
| "metrics_an.accuracy.hist(bins=20, alpha=0.4, label='analogs');\n", | |
| "metrics_mm.accuracy.hist(bins=20, alpha=0.4, label='moment matching', color='r')\n", | |
| "metrics_ostd.accuracy.hist(bins=20, alpha=0.4, label='one standard dev.', color='g')\n", | |
| "plt.legend(loc='upper left');" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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1TxifTG5nJFpux9RybCB9CKZO6/EZkiQEIYQQgCQEo9HyfCpajg1kLiNTp/X4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\naXkstJZjA3kOwdRpPT5Dkrm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4+LS5GpqpxgZQV1fHd999x3vvvceMGTN44YUXiIqK0ivT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\nkIQghBDi3yQhCCGEAOD/A7WM+/K9o1/AAAAAAElFTkSuQmCC\n" | |
| } | |
| ], | |
| "prompt_number": 106 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Over forecast" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# hist of when the observed wind was less than per-time minimum of the scenarios\n", | |
| "metrics_ostd.spread_over.hist(bins=20, alpha=0.4, label='one standard dev.', color='g')\n", | |
| "metrics_an.spread_over.hist(bins=20, alpha=0.4, label='analogs')\n", | |
| "metrics_mm.spread_over.hist(bins=20, alpha=0.4, label='moment matching', color='r')\n", | |
| "plt.legend();" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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0Fenctewue/OTM+ulLE9xen8FewpYsWSF0+sJIYTRTCYTHZRrp3V45v/pp5+S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\nowXjcwAAAABJRU5ErkJggg==\n" | |
| } | |
| ], | |
| "prompt_number": 98 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Under forecast" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# hist of when the observed wind was greater than the per-time maximum of the scenarios\n", | |
| "metrics_ostd.spread_under.hist(bins=20, alpha=0.4, label='one standard dev.', color='g')\n", | |
| "metrics_an.spread_under.hist(bins=20, alpha=0.4, label='analogs')\n", | |
| "metrics_mm.spread_under.hist(bins=20, alpha=0.4, label='moment matching', color='r')\n", | |
| "plt.legend();" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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n1M/W6Dcw3Kbt81WZFOd0smn/4pzDnDySCEA3b39sZdI0TWtup6qqKm699VZe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\nLC1/bXmrJ+VKWJlA7PJYhx+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| |
| } | |
| ], | |
| "prompt_number": 99 | |
| } | |
| ], | |
| "metadata": {} | |
| } | |
| ] | |
| } |
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