def production_model(): # figure 3 plt.close('all') cols = ['prod24h_before', 'Tout24hdiff', 'vWind24hdiff', 'sunRad24hdiff'] ts1 = ens.gen_hourly_timesteps(dt.datetime(2015, 12, 17, 1), dt.datetime(2016, 1, 15, 0)) ts2 = ens.gen_hourly_timesteps(dt.datetime(2016, 1, 20, 1), dt.datetime(2016, 2, 5, 0)) #load the data fit_data = ens.repack_ens_mean_as_df() fit_data['prod24h_before'] = sq.fetch_production( ts1[0] + dt.timedelta(days=-1), ts1[-1] + dt.timedelta(days=-1)) fit_data['Tout24hdiff'] = fit_data['Tout'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts1[0]+dt.timedelta(days=-1),\ ts_end=ts1[-1]+dt.timedelta(days=-1), \ weathervars=['Tout']).mean(axis=1) fit_data['vWind24hdiff'] = fit_data['vWind'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts1[0]+dt.timedelta(days=-1),\ ts_end=ts1[-1]+dt.timedelta(days=-1), \ weathervars=['vWind']).mean(axis=1) fit_data['sunRad24hdiff'] = fit_data['sunRad'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts1[0]+dt.timedelta(days=-1),\ ts_end=ts1[-1]+dt.timedelta(days=-1), \ weathervars=['sunRad']).mean(axis=1) vali_data = ens.repack_ens_mean_as_df(ts2[0], ts2[-1]) vali_data['prod24h_before'] = sq.fetch_production( ts2[0] + dt.timedelta(days=-1), ts2[-1] + dt.timedelta(days=-1)) vali_data['Tout24hdiff'] = vali_data['Tout'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts2[0]+dt.timedelta(days=-1),\ ts_end=ts2[-1]+dt.timedelta(days=-1), \ weathervars=['Tout']).mean(axis=1) vali_data['vWind24hdiff'] = vali_data['vWind'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts2[0]+dt.timedelta(days=-1),\ ts_end=ts2[-1]+dt.timedelta(days=-1), \ weathervars=['vWind']).mean(axis=1) vali_data['sunRad24hdiff'] = vali_data['sunRad'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts2[0]+dt.timedelta(days=-1),\ ts_end=ts2[-1]+dt.timedelta(days=-1), \ weathervars=['sunRad']).mean(axis=1) # correct error in production: new_val = (vali_data['prod'][116] + vali_data['prod'][116]) / 2 vali_data['prod'][116] = new_val vali_data['prod'][117] = new_val vali_data['prod24h_before'][116 + 24] = new_val vali_data['prod24h_before'][117 + 24] = new_val # do the fit X = fit_data[cols] y = fit_data['prod'] res = mlin_regression(y, X, add_const=False) fig, [ax1, ax2] = plt.subplots(2, 1, sharex=True, figsize=(dcolwidth, 0.57 * dcolwidth), gridspec_kw={'height_ratios': [4, 1]}) # load ensemble data ens_data1 = ens.load_ens_timeseries_as_df(ts_start=ts1[0], ts_end=ts1[-1],\ weathervars=['Tout', 'vWind', 'sunRad']) ens_data1['prod24h_before'] = fit_data['prod24h_before'] ens_data1_24h_before = ens.load_ens_timeseries_as_df(\ ts_start=ts1[0]+dt.timedelta(days=-1),\ ts_end=ts1[-1]+dt.timedelta(days=-1), \ weathervars=['Tout', 'vWind', 'sunRad']) ens_data2 = ens.load_ens_timeseries_as_df(ts_start=ts2[0], ts_end=ts2[-1],\ weathervars=['Tout', 'vWind', 'sunRad']) ens_data2['prod24h_before'] = vali_data['prod24h_before'] ens_data2_24h_before = ens.load_ens_timeseries_as_df(\ ts_start=ts2[0]+dt.timedelta(days=-1),\ ts_end=ts2[-1]+dt.timedelta(days=-1), \ weathervars=['Tout', 'vWind', 'sunRad']) for i in range(25): for v in ['Tout', 'vWind', 'sunRad']: key_raw = v + str(i) key_diff = v + '24hdiff' + str(i) ens_data1[ key_diff] = ens_data1[key_raw] - ens_data1_24h_before[key_raw] ens_data2[ key_diff] = ens_data2[key_raw] - ens_data2_24h_before[key_raw] all_ens_data = pd.concat([ens_data1, ens_data2]) all_ts = ts1 + ts2 # # # calculate production for each ensemble member ens_prods = np.zeros((len(all_ts), 25)) for i in range(25): ens_cols = ['Tout24hdiff' + str(i), 'vWind24hdiff' + str(i),\ 'sunRad24hdiff' + str(i), 'prod24h_before'] ens_params = pd.Series({ 'Tout24hdiff' + str(i): res.params['Tout24hdiff'], 'vWind24hdiff' + str(i): res.params['vWind24hdiff'], 'sunRad24hdiff' + str(i): res.params['sunRad24hdiff'], 'prod24h_before': res.params['prod24h_before'] }) ens_prods[:, i] = linear_map(all_ens_data, ens_params, ens_cols) # calculate combined confint ens_std = ens_prods.std(axis=1) vali_resid = linear_map(vali_data, res.params, cols) - vali_data['prod'] vali_resid_corrig = vali_resid - np.sign( vali_resid) * 1.9599 * ens_std[len(ts1):] #mean_conf_int_spread = (vali_resid_corrig.quantile(0.95) - vali_resid_corrig.quantile(0.05))/2 # this conf_int is not used anymore fit_resid = res.resid fit_resid_corrig = fit_resid - np.sign( fit_resid) * 1.9599 * ens_std[0:len(ts1)] conf_int_spread_lower = -fit_resid_corrig.quantile(0.025) conf_int_spread_higher = fit_resid_corrig.quantile(0.975) combined_conf_ints = conf_int_spread_lower + conf_int_spread_higher + 2 * 1.9599 * ens_std all_prod_model = np.concatenate( [res.fittedvalues, linear_map(vali_data, res.params, cols)]) combined_ub95 = all_prod_model + conf_int_spread_higher + 1.9599 * ens_std combined_lb95 = all_prod_model - (conf_int_spread_lower + 1.9599 * ens_std) # plot confint ax1.fill_between(all_ts[len(ts1):], combined_lb95[len(ts1):], combined_ub95[len(ts1):], label='95% prediction intervals') ax1.fill_between(all_ts[len(ts1):], all_prod_model[len(ts1):] - 1.9599 * ens_std[len(ts1):], all_prod_model[len(ts1):] + 1.9599 * ens_std[len(ts1):], facecolor='grey', label='Weather ensemble 95% conf. int.') # plot ensempble models ax1.plot_date(all_ts[len(ts1):], ens_prods[len(ts1):], '-', lw=0.5) ax1.plot_date(ts2, vali_data['prod'], 'k-', lw=2, label='Historical production') ax1.plot_date(ts2, linear_map(vali_data, res.params, cols), '-', c=red, lw=2, label='Production model') ax1.set_ylabel('Production [MW]', size=8) ax1.tick_params(axis='both', which='major', labelsize=8) ax1.xaxis.set_major_formatter(DateFormatter('%b %d')) ax1.legend(loc=1, prop={'size': 8}) ax1.set_ylim([300, 1100]) N = conf_int_spread_higher + 1.9599 * ens_std[len(ts1):].max() ax2.fill_between(ts2, -(1.9599 * ens_std[len(ts1):] + conf_int_spread_lower) / N, -1.9599 * ens_std[len(ts1):] / N, alpha=0.5) ax2.fill_between(ts2, -1.9599 * ens_std[len(ts1):] / N, np.zeros(len(ts2)), facecolor='grey', alpha=0.5) ax2.fill_between(ts2, 1.9599 * ens_std[len(ts1):] / N, facecolor='grey') ax2.fill_between(ts2, 1.9599 * ens_std[len(ts1):] / N, (conf_int_spread_higher + 1.9599 * ens_std[len(ts1):]) / N) ax2.set_ylabel('Prediction intervals \n[normalized]', size=8) ax2.tick_params(axis='y', which='major', labelsize=8) ax2.set_xlim(dt.datetime(2016, 1, 20, 0), dt.datetime(2016, 2, 5, 0)) fig.tight_layout() print "Min_normalized pos conf bound. ", np.min(1.9599 * ens_std[len(ts1):] / N + conf_int_spread_higher / N) print "MAE = " + str(mae(vali_resid)) print "MAPE = " + str(mape(vali_resid, vali_data['prod'])) print "RMSE = " + str(rmse(vali_resid)) print "ME = " + str(np.mean(vali_resid)) print "MAE (fit) = " + str(mae(res.resid)) print "MAPE (fit) = " + str(mape(res.resid, fit_data['prod'])) print "RMSE (fit)= " + str(rmse(res.resid)) print "ME (fit)= " + str(np.mean(res.resid)) print "Width of const blue bands (MW)", conf_int_spread_lower, conf_int_spread_higher plt.savefig('Q:/Projekter/Ens Article 1/figures/production_model.pdf', dpi=400) EO3_fc1 = sq.fetch_EO3_midnight_forecast(ts1[0], ts1[-1]) EO3_fc2 = sq.fetch_EO3_midnight_forecast(ts2[0], ts2[-1]) EO3_err = EO3_fc2 - vali_data['prod'] EO3_err_fit = EO3_fc1 - fit_data['prod'] print "MAE (EO3) = " + str(mae(EO3_err)) print "MAPE (EO3) = " + str(mape(EO3_err, vali_data['prod'])) print "RMSE (EO3)= " + str(rmse(EO3_err)) print "ME (EO3)= " + str(np.mean(EO3_err)) print "MAE (EO3_fit) = " + str(mae(EO3_err_fit)) print "MAPE (EO3_fit) = " + str(mape(EO3_err_fit, fit_data['prod'])) print "RMSE (EO3_fit)= " + str(rmse(EO3_err_fit)) print "ME (EO3_fit)= " + str(np.mean(EO3_err_fit)) print np.min(combined_conf_ints[len(ts1):] / combined_conf_ints.max()) np.savez('combined_conf_int', combined_conf_int=(conf_int_spread_higher + 1.9599 * ens_std), timesteps=all_ts) print "Corr coeff: vali ", np.corrcoef( vali_data['prod'], linear_map(vali_data, res.params, cols))[0, 1] print "Corr coeff: vali EO3 ", np.corrcoef(vali_data['prod'], EO3_fc2)[0, 1] print "Corr coeff: fit ", np.corrcoef(fit_data['prod'], res.fittedvalues)[0, 1] print "Corr coeff: fit EO3 ", np.corrcoef(fit_data['prod'], EO3_fc1)[0, 1] print "% of actual production in vali period above upper", float( len( np.where(vali_data['prod'] > (conf_int_spread_higher + 1.9599 * ens_std[len(ts1):] + linear_map(vali_data, res.params, cols)))[0])) / len(ts2) print "plus minus: ", 0.5 / len(ts2) print "% of actual production in vali period below lower", float( len( np.where(vali_data['prod'] < (linear_map(vali_data, res.params, cols) - (conf_int_spread_lower + 1.9599 * ens_std[len(ts1):]))) [0])) / len(ts2) print "plus minus: ", 0.5 / len(ts2) return res, fit_data
def second_ens_prod_fig(): """ This plot is based on a production model taking into account: the production 24 hours before as well as the change in temparature, windspeed and solar radiotion from 24 hours ago to now. """ plt.close('all') cols = ['prod24h_before', 'Tout24hdiff', 'vWind24hdiff', 'sunRad24hdiff'] ts1 = ens.gen_hourly_timesteps(dt.datetime(2015, 12, 17, 1), dt.datetime(2016, 1, 15, 0)) ts2 = ens.gen_hourly_timesteps(dt.datetime(2016, 1, 20, 1), dt.datetime(2016, 2, 5, 0)) #load the data fit_data = ens.repack_ens_mean_as_df() fit_data['prod24h_before'] = sq.fetch_production( ts1[0] + dt.timedelta(days=-1), ts1[-1] + dt.timedelta(days=-1)) fit_data['Tout24hdiff'] = fit_data['Tout'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts1[0]+dt.timedelta(days=-1),\ ts_end=ts1[-1]+dt.timedelta(days=-1), \ weathervars=['Tout']).mean(axis=1) fit_data['vWind24hdiff'] = fit_data['vWind'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts1[0]+dt.timedelta(days=-1),\ ts_end=ts1[-1]+dt.timedelta(days=-1), \ weathervars=['vWind']).mean(axis=1) fit_data['sunRad24hdiff'] = fit_data['sunRad'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts1[0]+dt.timedelta(days=-1),\ ts_end=ts1[-1]+dt.timedelta(days=-1), \ weathervars=['sunRad']).mean(axis=1) vali_data = ens.repack_ens_mean_as_df(ts2[0], ts2[-1]) vali_data['prod24h_before'] = sq.fetch_production( ts2[0] + dt.timedelta(days=-1), ts2[-1] + dt.timedelta(days=-1)) vali_data['Tout24hdiff'] = vali_data['Tout'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts2[0]+dt.timedelta(days=-1),\ ts_end=ts2[-1]+dt.timedelta(days=-1), \ weathervars=['Tout']).mean(axis=1) vali_data['vWind24hdiff'] = vali_data['vWind'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts2[0]+dt.timedelta(days=-1),\ ts_end=ts2[-1]+dt.timedelta(days=-1), \ weathervars=['vWind']).mean(axis=1) vali_data['sunRad24hdiff'] = vali_data['sunRad'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts2[0]+dt.timedelta(days=-1),\ ts_end=ts2[-1]+dt.timedelta(days=-1), \ weathervars=['sunRad']).mean(axis=1) # correct error in production: new_val = (vali_data['prod'][116] + vali_data['prod'][116]) / 2 vali_data['prod'][116] = new_val vali_data['prod'][117] = new_val vali_data['prod24h_before'][116 + 24] = new_val vali_data['prod24h_before'][117 + 24] = new_val # do the fit X = fit_data[cols] y = fit_data['prod'] res = mlin_regression(y, X, add_const=False) fig, [ax1, ax2] = plt.subplots(2, 1, figsize=(40, 20)) # load ensemble data ens_data1 = ens.load_ens_timeseries_as_df(ts_start=ts1[0], ts_end=ts1[-1],\ weathervars=['Tout', 'vWind', 'sunRad']) ens_data1['prod24h_before'] = fit_data['prod24h_before'] ens_data1_24h_before = ens.load_ens_timeseries_as_df(\ ts_start=ts1[0]+dt.timedelta(days=-1),\ ts_end=ts1[-1]+dt.timedelta(days=-1), \ weathervars=['Tout', 'vWind', 'sunRad']) ens_data2 = ens.load_ens_timeseries_as_df(ts_start=ts2[0], ts_end=ts2[-1],\ weathervars=['Tout', 'vWind', 'sunRad']) ens_data2['prod24h_before'] = vali_data['prod24h_before'] ens_data2_24h_before = ens.load_ens_timeseries_as_df(\ ts_start=ts2[0]+dt.timedelta(days=-1),\ ts_end=ts2[-1]+dt.timedelta(days=-1), \ weathervars=['Tout', 'vWind', 'sunRad']) for i in range(25): for v in ['Tout', 'vWind', 'sunRad']: key_raw = v + str(i) key_diff = v + '24hdiff' + str(i) ens_data1[ key_diff] = ens_data1[key_raw] - ens_data1_24h_before[key_raw] ens_data2[ key_diff] = ens_data2[key_raw] - ens_data2_24h_before[key_raw] all_ens_data = pd.concat([ens_data1, ens_data2]) all_ts = ts1 + ts2 # # # calculate production for each ensemble member ens_prods = np.zeros((len(all_ts), 25)) for i in range(25): ens_cols = ['Tout24hdiff' + str(i), 'vWind24hdiff' + str(i),\ 'sunRad24hdiff' + str(i), 'prod24h_before'] ens_params = pd.Series({ 'Tout24hdiff' + str(i): res.params['Tout24hdiff'], 'vWind24hdiff' + str(i): res.params['vWind24hdiff'], 'sunRad24hdiff' + str(i): res.params['sunRad24hdiff'], 'prod24h_before': res.params['prod24h_before'] }) ens_prods[:, i] = linear_map(all_ens_data, ens_params, ens_cols) # calculate combined confint ens_std = ens_prods.std(axis=1) vali_resid = linear_map(vali_data, res.params, cols) - vali_data['prod'] vali_resid_corrig = vali_resid - np.sign( vali_resid) * 1.9599 * ens_std[len(ts1):] mean_conf_int_spread = (vali_resid_corrig.quantile(0.95) - vali_resid_corrig.quantile(0.05)) / 2 combined_conf_int = mean_conf_int_spread + 1.9599 * ens_std all_prod_model = np.concatenate( [res.fittedvalues, linear_map(vali_data, res.params, cols)]) combined_ub95 = all_prod_model + combined_conf_int combined_lb95 = all_prod_model - combined_conf_int # plot confint ax1.fill_between(all_ts, combined_lb95, combined_ub95, label='Combined 95% conf. int.') ax1.fill_between(all_ts, all_prod_model - 1.9599 * ens_std, all_prod_model + 1.9599 * ens_std, facecolor='grey', label='Ensemble 95% conf. int.') # plot ensempble models ax1.plot_date(all_ts, ens_prods, '-', lw=0.5) ax1.plot_date(ts1, y, 'k-', lw=2, label='Actual production') ax1.plot_date(ts1, res.fittedvalues, 'r-', lw=2, label='Model on ensemble mean') ax1.plot_date(ts2, vali_data['prod'], 'k-', lw=2, label='') ax1.plot_date(ts2, linear_map(vali_data, res.params, cols), 'r-', lw=2) ax1.set_ylabel('[MW]') ax1.legend(loc=2) ax1.set_ylim([0, 1100]) ax2.plot_date(ts1, res.resid, '-', label='Residual, fitted data') ax2.plot_date(ts2, vali_resid, '-', label='Residual, validation data') ax2.set_ylabel('[MW]') ax2.legend(loc=2) ax2.set_ylim([-550, 550]) print "MAE = " + str(mae(vali_resid)) print "MAPE = " + str(mape(vali_resid, vali_data['prod'])) print "RMSE = " + str(rmse(vali_resid)) print "ME = " + str(np.mean(vali_resid)) print "MAE (fit) = " + str(mae(res.resid)) print "MAPE (fit) = " + str(mape(res.resid, fit_data['prod'])) print "RMSE (fit)= " + str(rmse(res.resid)) print "ME (fit)= " + str(np.mean(res.resid)) plt.savefig('figures/ens_prod_models_v2.pdf', dpi=600) plt.figure() plt.plot_date(all_ts, ens_std) plt.ylabel('Std. of ensemble production models [MW]') plt.savefig('figures/std_ens_prod_models.pdf', dpi=600) # vali_ens_std = ens_std[len(ts1):] sns.jointplot(x=pd.Series(vali_ens_std), y=np.abs(vali_resid)) sns.jointplot(x=vali_data['prod'], y=pd.Series(linear_map(vali_data, res.params, cols))) EO3_fc1 = sq.fetch_EO3_midnight_forecast(ts1[0], ts1[-1]) EO3_fc2 = sq.fetch_EO3_midnight_forecast(ts2[0], ts2[-1]) plt.figure() plt.plot_date(ts1, fit_data['prod'], 'k-', label='Actual production') plt.plot_date(ts2, vali_data['prod'], 'k-') plt.plot_date(ts1, EO3_fc1, 'r-', label='EO3 forecast') plt.plot_date(ts2, EO3_fc2, 'r-') EO3_err = EO3_fc2 - vali_data['prod'] EO3_err_fit = EO3_fc1 - fit_data['prod'] print "MAE (EO3) = " + str(mae(EO3_err)) print "MAPE (EO3) = " + str(mape(EO3_err, vali_data['prod'])) print "RMSE (EO3)= " + str(rmse(EO3_err)) print "ME (EO3)= " + str(np.mean(EO3_err)) print "MAE (EO3_fit) = " + str(mae(EO3_err_fit)) print "MAPE (EO3_fit) = " + str(mape(EO3_err_fit, fit_data['prod'])) print "RMSE (EO3_fit)= " + str(rmse(EO3_err_fit)) print "ME (EO3_fit)= " + str(np.mean(EO3_err_fit)) sns.jointplot(x=pd.Series(vali_ens_std), y=np.abs(EO3_err)) plt.figure(figsize=(20, 10)) plt.subplot(2, 1, 1) plt.plot_date(all_ts, combined_conf_int / combined_conf_int.max(), '-') plt.ylabel('Model + ensemble uncertainty \n [normalized]') plt.ylim(0, 1) plt.subplot(2, 1, 2) plt.plot_date(all_ts, (1 - 0.2 * combined_conf_int / combined_conf_int.max()), '-', label='Dynamic setpoint') plt.plot_date(all_ts, 0.8 * np.ones(len(all_ts)), '--', label='Static setpoint') plt.ylabel( 'Setpoint for pump massflow \n temperature [fraction of max pump cap]') plt.legend() plt.ylim(.7, 1) plt.savefig('figures/setpoint.pdf') return vali_data, fit_data, res, ens_std, vali_resid
def production_model(): # figure 3 plt.close('all') cols = ['prod24h_before', 'Tout24hdiff', 'vWind24hdiff', 'sunRad24hdiff'] ts1 = ens.gen_hourly_timesteps(dt.datetime(2015,12,17,1), dt.datetime(2016,1,15,0)) ts2 = ens.gen_hourly_timesteps(dt.datetime(2016,1,20,1), dt.datetime(2016,2,5,0)) #load the data fit_data = ens.repack_ens_mean_as_df() fit_data['prod24h_before'] = sq.fetch_production(ts1[0]+dt.timedelta(days=-1), ts1[-1]+dt.timedelta(days=-1)) fit_data['Tout24hdiff'] = fit_data['Tout'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts1[0]+dt.timedelta(days=-1),\ ts_end=ts1[-1]+dt.timedelta(days=-1), \ weathervars=['Tout']).mean(axis=1) fit_data['vWind24hdiff'] = fit_data['vWind'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts1[0]+dt.timedelta(days=-1),\ ts_end=ts1[-1]+dt.timedelta(days=-1), \ weathervars=['vWind']).mean(axis=1) fit_data['sunRad24hdiff'] = fit_data['sunRad'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts1[0]+dt.timedelta(days=-1),\ ts_end=ts1[-1]+dt.timedelta(days=-1), \ weathervars=['sunRad']).mean(axis=1) vali_data = ens.repack_ens_mean_as_df(ts2[0], ts2[-1]) vali_data['prod24h_before'] = sq.fetch_production(ts2[0]+dt.timedelta(days=-1), ts2[-1]+dt.timedelta(days=-1)) vali_data['Tout24hdiff'] = vali_data['Tout'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts2[0]+dt.timedelta(days=-1),\ ts_end=ts2[-1]+dt.timedelta(days=-1), \ weathervars=['Tout']).mean(axis=1) vali_data['vWind24hdiff'] = vali_data['vWind'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts2[0]+dt.timedelta(days=-1),\ ts_end=ts2[-1]+dt.timedelta(days=-1), \ weathervars=['vWind']).mean(axis=1) vali_data['sunRad24hdiff'] = vali_data['sunRad'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts2[0]+dt.timedelta(days=-1),\ ts_end=ts2[-1]+dt.timedelta(days=-1), \ weathervars=['sunRad']).mean(axis=1) # correct error in production: new_val = (vali_data['prod'][116] +vali_data['prod'][116])/2 vali_data['prod'][116] = new_val vali_data['prod'][117] = new_val vali_data['prod24h_before'][116+24] = new_val vali_data['prod24h_before'][117+24] = new_val # do the fit X = fit_data[cols] y = fit_data['prod'] res = mlin_regression(y, X, add_const=False) fig, [ax1, ax2] = plt.subplots(2, 1, sharex=True, figsize=(dcolwidth, 0.57*dcolwidth), gridspec_kw={'height_ratios':[4,1]}) # load ensemble data ens_data1 = ens.load_ens_timeseries_as_df(ts_start=ts1[0], ts_end=ts1[-1],\ weathervars=['Tout', 'vWind', 'sunRad']) ens_data1['prod24h_before'] = fit_data['prod24h_before'] ens_data1_24h_before = ens.load_ens_timeseries_as_df(\ ts_start=ts1[0]+dt.timedelta(days=-1),\ ts_end=ts1[-1]+dt.timedelta(days=-1), \ weathervars=['Tout', 'vWind', 'sunRad']) ens_data2 = ens.load_ens_timeseries_as_df(ts_start=ts2[0], ts_end=ts2[-1],\ weathervars=['Tout', 'vWind', 'sunRad']) ens_data2['prod24h_before'] = vali_data['prod24h_before'] ens_data2_24h_before = ens.load_ens_timeseries_as_df(\ ts_start=ts2[0]+dt.timedelta(days=-1),\ ts_end=ts2[-1]+dt.timedelta(days=-1), \ weathervars=['Tout', 'vWind', 'sunRad']) for i in range(25): for v in ['Tout', 'vWind', 'sunRad']: key_raw = v + str(i) key_diff = v + '24hdiff' + str(i) ens_data1[key_diff] = ens_data1[key_raw] - ens_data1_24h_before[key_raw] ens_data2[key_diff] = ens_data2[key_raw] - ens_data2_24h_before[key_raw] all_ens_data = pd.concat([ens_data1, ens_data2]) all_ts = ts1 + ts2 # # # calculate production for each ensemble member ens_prods = np.zeros((len(all_ts), 25)) for i in range(25): ens_cols = ['Tout24hdiff' + str(i), 'vWind24hdiff' + str(i),\ 'sunRad24hdiff' + str(i), 'prod24h_before'] ens_params = pd.Series({'Tout24hdiff' + str(i):res.params['Tout24hdiff'], 'vWind24hdiff' + str(i):res.params['vWind24hdiff'], 'sunRad24hdiff' + str(i):res.params['sunRad24hdiff'], 'prod24h_before':res.params['prod24h_before']}) ens_prods[:,i] = linear_map(all_ens_data, ens_params, ens_cols) # calculate combined confint ens_std = ens_prods.std(axis=1) vali_resid = linear_map(vali_data, res.params, cols) - vali_data['prod'] vali_resid_corrig = vali_resid - np.sign(vali_resid)*1.9599*ens_std[len(ts1):] #mean_conf_int_spread = (vali_resid_corrig.quantile(0.95) - vali_resid_corrig.quantile(0.05))/2 # this conf_int is not used anymore fit_resid = res.resid fit_resid_corrig = fit_resid - np.sign(fit_resid)*1.9599*ens_std[0:len(ts1)] conf_int_spread_lower = - fit_resid_corrig.quantile(0.025) conf_int_spread_higher = fit_resid_corrig.quantile(0.975) combined_conf_ints = conf_int_spread_lower + conf_int_spread_higher + 2*1.9599*ens_std all_prod_model = np.concatenate([res.fittedvalues, linear_map(vali_data, res.params, cols)]) combined_ub95 = all_prod_model + conf_int_spread_higher + 1.9599*ens_std combined_lb95 = all_prod_model - (conf_int_spread_lower + 1.9599*ens_std) # plot confint ax1.fill_between(all_ts[len(ts1):], combined_lb95[len(ts1):], combined_ub95[len(ts1):], label='95% prediction intervals') ax1.fill_between(all_ts[len(ts1):], all_prod_model[len(ts1):] - 1.9599*ens_std[len(ts1):], all_prod_model[len(ts1):] + 1.9599*ens_std[len(ts1):], facecolor='grey', label='Weather ensemble 95% conf. int.') # plot ensempble models ax1.plot_date(all_ts[len(ts1):], ens_prods[len(ts1):], '-', lw=0.5) ax1.plot_date(ts2, vali_data['prod'], 'k-', lw=2, label='Historical production') ax1.plot_date(ts2, linear_map(vali_data, res.params, cols), '-', c=red, lw=2, label='Production model') ax1.set_ylabel('Production [MW]', size=8) ax1.tick_params(axis='both', which='major', labelsize=8) ax1.xaxis.set_major_formatter(DateFormatter('%b %d') ) ax1.legend(loc=1, prop={'size':8}) ax1.set_ylim([300,1100]) N = conf_int_spread_higher + 1.9599*ens_std[len(ts1):].max() ax2.fill_between(ts2, -(1.9599*ens_std[len(ts1):]+conf_int_spread_lower)/N, -1.9599*ens_std[len(ts1):]/N, alpha=0.5) ax2.fill_between(ts2, -1.9599*ens_std[len(ts1):]/N, np.zeros(len(ts2)), facecolor='grey',alpha=0.5) ax2.fill_between(ts2, 1.9599*ens_std[len(ts1):]/N, facecolor='grey') ax2.fill_between(ts2, 1.9599*ens_std[len(ts1):]/N, (conf_int_spread_higher+1.9599*ens_std[len(ts1):])/N) ax2.set_ylabel('Prediction intervals \n[normalized]', size=8) ax2.tick_params(axis='y', which='major', labelsize=8) ax2.set_xlim(dt.datetime(2016,1,20,0), dt.datetime(2016,2,5,0)) fig.tight_layout() print "Min_normalized pos conf bound. ", np.min(1.9599*ens_std[len(ts1):]/N+conf_int_spread_higher/N) print "MAE = " + str(mae(vali_resid)) print "MAPE = " + str(mape(vali_resid, vali_data['prod'])) print "RMSE = " + str(rmse(vali_resid)) print "ME = " + str(np.mean(vali_resid)) print "MAE (fit) = " + str(mae(res.resid)) print "MAPE (fit) = " + str(mape(res.resid, fit_data['prod'])) print "RMSE (fit)= " + str(rmse(res.resid)) print "ME (fit)= " + str(np.mean(res.resid)) print "Width of const blue bands (MW)", conf_int_spread_lower, conf_int_spread_higher plt.savefig('Q:/Projekter/Ens Article 1/figures/production_model.pdf', dpi=400) EO3_fc1 = sq.fetch_EO3_midnight_forecast(ts1[0], ts1[-1]) EO3_fc2 = sq.fetch_EO3_midnight_forecast(ts2[0], ts2[-1]) EO3_err = EO3_fc2-vali_data['prod'] EO3_err_fit = EO3_fc1-fit_data['prod'] print "MAE (EO3) = " + str(mae(EO3_err)) print "MAPE (EO3) = " + str(mape(EO3_err, vali_data['prod'])) print "RMSE (EO3)= " + str(rmse(EO3_err)) print "ME (EO3)= " + str(np.mean(EO3_err)) print "MAE (EO3_fit) = " + str(mae(EO3_err_fit)) print "MAPE (EO3_fit) = " + str(mape(EO3_err_fit, fit_data['prod'])) print "RMSE (EO3_fit)= " + str(rmse(EO3_err_fit)) print "ME (EO3_fit)= " + str(np.mean(EO3_err_fit)) print np.min(combined_conf_ints[len(ts1):]/combined_conf_ints.max()) np.savez('combined_conf_int', combined_conf_int=(conf_int_spread_higher+1.9599*ens_std), timesteps=all_ts) print "Corr coeff: vali ", np.corrcoef(vali_data['prod'],linear_map(vali_data, res.params, cols))[0,1] print "Corr coeff: vali EO3 ", np.corrcoef(vali_data['prod'], EO3_fc2)[0,1] print "Corr coeff: fit ", np.corrcoef(fit_data['prod'],res.fittedvalues)[0,1] print "Corr coeff: fit EO3 ", np.corrcoef(fit_data['prod'], EO3_fc1)[0,1] print "% of actual production in vali period above upper", float(len(np.where(vali_data['prod']>(conf_int_spread_higher+1.9599*ens_std[len(ts1):]+linear_map(vali_data, res.params, cols)))[0]))/len(ts2) print "plus minus: ", 0.5/len(ts2) print "% of actual production in vali period below lower", float(len(np.where(vali_data['prod']<(linear_map(vali_data, res.params, cols)-(conf_int_spread_lower+1.9599*ens_std[len(ts1):])))[0]))/len(ts2) print "plus minus: ", 0.5/len(ts2) return res, fit_data
def second_ens_prod_fig(): """ This plot is based on a production model taking into account: the production 24 hours before as well as the change in temparature, windspeed and solar radiotion from 24 hours ago to now. """ plt.close('all') cols = ['prod24h_before', 'Tout24hdiff', 'vWind24hdiff', 'sunRad24hdiff'] ts1 = ens.gen_hourly_timesteps(dt.datetime(2015,12,17,1), dt.datetime(2016,1,15,0)) ts2 = ens.gen_hourly_timesteps(dt.datetime(2016,1,20,1), dt.datetime(2016,2,5,0)) #load the data fit_data = ens.repack_ens_mean_as_df() fit_data['prod24h_before'] = sq.fetch_production(ts1[0]+dt.timedelta(days=-1), ts1[-1]+dt.timedelta(days=-1)) fit_data['Tout24hdiff'] = fit_data['Tout'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts1[0]+dt.timedelta(days=-1),\ ts_end=ts1[-1]+dt.timedelta(days=-1), \ weathervars=['Tout']).mean(axis=1) fit_data['vWind24hdiff'] = fit_data['vWind'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts1[0]+dt.timedelta(days=-1),\ ts_end=ts1[-1]+dt.timedelta(days=-1), \ weathervars=['vWind']).mean(axis=1) fit_data['sunRad24hdiff'] = fit_data['sunRad'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts1[0]+dt.timedelta(days=-1),\ ts_end=ts1[-1]+dt.timedelta(days=-1), \ weathervars=['sunRad']).mean(axis=1) vali_data = ens.repack_ens_mean_as_df(ts2[0], ts2[-1]) vali_data['prod24h_before'] = sq.fetch_production(ts2[0]+dt.timedelta(days=-1), ts2[-1]+dt.timedelta(days=-1)) vali_data['Tout24hdiff'] = vali_data['Tout'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts2[0]+dt.timedelta(days=-1),\ ts_end=ts2[-1]+dt.timedelta(days=-1), \ weathervars=['Tout']).mean(axis=1) vali_data['vWind24hdiff'] = vali_data['vWind'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts2[0]+dt.timedelta(days=-1),\ ts_end=ts2[-1]+dt.timedelta(days=-1), \ weathervars=['vWind']).mean(axis=1) vali_data['sunRad24hdiff'] = vali_data['sunRad'] \ - ens.load_ens_timeseries_as_df(\ ts_start=ts2[0]+dt.timedelta(days=-1),\ ts_end=ts2[-1]+dt.timedelta(days=-1), \ weathervars=['sunRad']).mean(axis=1) # correct error in production: new_val = (vali_data['prod'][116] +vali_data['prod'][116])/2 vali_data['prod'][116] = new_val vali_data['prod'][117] = new_val vali_data['prod24h_before'][116+24] = new_val vali_data['prod24h_before'][117+24] = new_val # do the fit X = fit_data[cols] y = fit_data['prod'] res = mlin_regression(y, X, add_const=False) fig, [ax1, ax2] = plt.subplots(2,1, figsize=(40,20)) # load ensemble data ens_data1 = ens.load_ens_timeseries_as_df(ts_start=ts1[0], ts_end=ts1[-1],\ weathervars=['Tout', 'vWind', 'sunRad']) ens_data1['prod24h_before'] = fit_data['prod24h_before'] ens_data1_24h_before = ens.load_ens_timeseries_as_df(\ ts_start=ts1[0]+dt.timedelta(days=-1),\ ts_end=ts1[-1]+dt.timedelta(days=-1), \ weathervars=['Tout', 'vWind', 'sunRad']) ens_data2 = ens.load_ens_timeseries_as_df(ts_start=ts2[0], ts_end=ts2[-1],\ weathervars=['Tout', 'vWind', 'sunRad']) ens_data2['prod24h_before'] = vali_data['prod24h_before'] ens_data2_24h_before = ens.load_ens_timeseries_as_df(\ ts_start=ts2[0]+dt.timedelta(days=-1),\ ts_end=ts2[-1]+dt.timedelta(days=-1), \ weathervars=['Tout', 'vWind', 'sunRad']) for i in range(25): for v in ['Tout', 'vWind', 'sunRad']: key_raw = v + str(i) key_diff = v + '24hdiff' + str(i) ens_data1[key_diff] = ens_data1[key_raw] - ens_data1_24h_before[key_raw] ens_data2[key_diff] = ens_data2[key_raw] - ens_data2_24h_before[key_raw] all_ens_data = pd.concat([ens_data1, ens_data2]) all_ts = ts1 + ts2 # # # calculate production for each ensemble member ens_prods = np.zeros((len(all_ts), 25)) for i in range(25): ens_cols = ['Tout24hdiff' + str(i), 'vWind24hdiff' + str(i),\ 'sunRad24hdiff' + str(i), 'prod24h_before'] ens_params = pd.Series({'Tout24hdiff' + str(i):res.params['Tout24hdiff'], 'vWind24hdiff' + str(i):res.params['vWind24hdiff'], 'sunRad24hdiff' + str(i):res.params['sunRad24hdiff'], 'prod24h_before':res.params['prod24h_before']}) ens_prods[:,i] = linear_map(all_ens_data, ens_params, ens_cols) # calculate combined confint ens_std = ens_prods.std(axis=1) vali_resid = linear_map(vali_data, res.params, cols) - vali_data['prod'] vali_resid_corrig = vali_resid - np.sign(vali_resid)*1.9599*ens_std[len(ts1):] mean_conf_int_spread = (vali_resid_corrig.quantile(0.95) - vali_resid_corrig.quantile(0.05))/2 combined_conf_int = mean_conf_int_spread + 1.9599*ens_std all_prod_model = np.concatenate([res.fittedvalues, linear_map(vali_data, res.params, cols)]) combined_ub95 = all_prod_model + combined_conf_int combined_lb95 = all_prod_model - combined_conf_int # plot confint ax1.fill_between(all_ts, combined_lb95, combined_ub95, label='Combined 95% conf. int.') ax1.fill_between(all_ts, all_prod_model - 1.9599*ens_std, all_prod_model + 1.9599*ens_std, facecolor='grey', label='Ensemble 95% conf. int.') # plot ensempble models ax1.plot_date(all_ts, ens_prods, '-', lw=0.5) ax1.plot_date(ts1, y, 'k-', lw=2, label='Actual production') ax1.plot_date(ts1, res.fittedvalues,'r-', lw=2, label='Model on ensemble mean') ax1.plot_date(ts2, vali_data['prod'], 'k-', lw=2, label='') ax1.plot_date(ts2, linear_map(vali_data, res.params, cols), 'r-', lw=2) ax1.set_ylabel('[MW]') ax1.legend(loc=2) ax1.set_ylim([0,1100]) ax2.plot_date(ts1, res.resid, '-', label='Residual, fitted data') ax2.plot_date(ts2, vali_resid, '-', label='Residual, validation data') ax2.set_ylabel('[MW]') ax2.legend(loc=2) ax2.set_ylim([-550, 550]) print "MAE = " + str(mae(vali_resid)) print "MAPE = " + str(mape(vali_resid, vali_data['prod'])) print "RMSE = " + str(rmse(vali_resid)) print "ME = " + str(np.mean(vali_resid)) print "MAE (fit) = " + str(mae(res.resid)) print "MAPE (fit) = " + str(mape(res.resid, fit_data['prod'])) print "RMSE (fit)= " + str(rmse(res.resid)) print "ME (fit)= " + str(np.mean(res.resid)) plt.savefig('figures/ens_prod_models_v2.pdf', dpi=600) plt.figure() plt.plot_date(all_ts, ens_std) plt.ylabel('Std. of ensemble production models [MW]') plt.savefig('figures/std_ens_prod_models.pdf', dpi=600) # vali_ens_std = ens_std[len(ts1):] sns.jointplot(x=pd.Series(vali_ens_std), y=np.abs(vali_resid)) sns.jointplot(x=vali_data['prod'], y=pd.Series(linear_map(vali_data, res.params, cols))) EO3_fc1 = sq.fetch_EO3_midnight_forecast(ts1[0], ts1[-1]) EO3_fc2 = sq.fetch_EO3_midnight_forecast(ts2[0], ts2[-1]) plt.figure() plt.plot_date(ts1, fit_data['prod'], 'k-', label='Actual production') plt.plot_date(ts2, vali_data['prod'], 'k-') plt.plot_date(ts1, EO3_fc1, 'r-', label='EO3 forecast') plt.plot_date(ts2, EO3_fc2, 'r-') EO3_err = EO3_fc2-vali_data['prod'] EO3_err_fit = EO3_fc1-fit_data['prod'] print "MAE (EO3) = " + str(mae(EO3_err)) print "MAPE (EO3) = " + str(mape(EO3_err, vali_data['prod'])) print "RMSE (EO3)= " + str(rmse(EO3_err)) print "ME (EO3)= " + str(np.mean(EO3_err)) print "MAE (EO3_fit) = " + str(mae(EO3_err_fit)) print "MAPE (EO3_fit) = " + str(mape(EO3_err_fit, fit_data['prod'])) print "RMSE (EO3_fit)= " + str(rmse(EO3_err_fit)) print "ME (EO3_fit)= " + str(np.mean(EO3_err_fit)) sns.jointplot(x=pd.Series(vali_ens_std), y=np.abs(EO3_err)) plt.figure(figsize=(20,10)) plt.subplot(2,1,1) plt.plot_date(all_ts, combined_conf_int/combined_conf_int.max(), '-') plt.ylabel('Model + ensemble uncertainty \n [normalized]') plt.ylim(0,1) plt.subplot(2,1,2) plt.plot_date(all_ts, (1-0.2*combined_conf_int/combined_conf_int.max()), '-', label='Dynamic setpoint') plt.plot_date(all_ts, 0.8*np.ones(len(all_ts)), '--', label='Static setpoint') plt.ylabel('Setpoint for pump massflow \n temperature [fraction of max pump cap]') plt.legend() plt.ylim(.7,1) plt.savefig('figures/setpoint.pdf') return vali_data, fit_data, res, ens_std, vali_resid
fit_y = fit_data['prod'] results = [] for columns in all_combs: X = fit_data[columns] res = mlin_regression(fit_y,X, add_const=False) results.append(res) vali_preds = [] for cols in all_combs: vali_pred = linear_map(vali_data, res.params, cols) vali_preds.append(vali_pred) rmses = [rmse(vp-vali_data['prod']) for vp in vali_preds] aics = [r.aic for r in results] for c,r,a in zip(all_combs, rmses, aics): print c,r,a right_columns = ['prod24h_before', 'Tout24hdiff', 'vWind24hdiff', 'sunRad24hdiff'] test_pred = linear_map(test_data, results[all_combs.index(right_columns)].params, right_columns) print "Test RMSE", rmse(test_pred-test_data['prod']) print "Test MAE", mae(test_pred-test_data['prod']) print "Test MAPE", mape(test_pred-test_data['prod'], test_data['prod']) EO3_fc_test = sq.fetch_EO3_midnight_forecast(test_ts[0], test_ts[-1]) EO3_err = EO3_fc_test-test_data['prod'] print "MAE (EO3) = " + str(mae(EO3_err)) print "MAPE (EO3) = " + str(mape(EO3_err, test_data['prod'])) print "RMSE (EO3)= " + str(rmse(EO3_err)) print "ME (EO3)= " + str(np.mean(EO3_err))
res = mlin_regression(fit_y, X, add_const=False) results.append(res) vali_preds = [] for cols in all_combs: vali_pred = linear_map(vali_data, res.params, cols) vali_preds.append(vali_pred) rmses = [rmse(vp - vali_data['prod']) for vp in vali_preds] aics = [r.aic for r in results] for c, r, a in zip(all_combs, rmses, aics): print c, r, a right_columns = [ 'prod24h_before', 'Tout24hdiff', 'vWind24hdiff', 'sunRad24hdiff' ] test_pred = linear_map(test_data, results[all_combs.index(right_columns)].params, right_columns) print "Test RMSE", rmse(test_pred - test_data['prod']) print "Test MAE", mae(test_pred - test_data['prod']) print "Test MAPE", mape(test_pred - test_data['prod'], test_data['prod']) EO3_fc_test = sq.fetch_EO3_midnight_forecast(test_ts[0], test_ts[-1]) EO3_err = EO3_fc_test - test_data['prod'] print "MAE (EO3) = " + str(mae(EO3_err)) print "MAPE (EO3) = " + str(mape(EO3_err, test_data['prod'])) print "RMSE (EO3)= " + str(rmse(EO3_err)) print "ME (EO3)= " + str(np.mean(EO3_err))