def eemeter_baseline_daily(file, temperature, install_start, install_end): """ This method uses linear regression to create daily load profile to serve as the counterfactual for the period where the new measure has been installed. The key part of this model is the temperature changepoint determined by the heating and cooling degree days (HDD & CDD). CalTRACK refers to a standardized model used in california to measure energy efficiency savings from various measures (i.e. LED lightbulbs, efficient appliances, weatherization, etc.). In this case it will be used to model energy growth instead of savings. """ file = 'data/cchp_daily.csv' daily_meter_data = eemeter.meter_data_from_csv(file, freq='daily') # get meter data suitable for fitting a baseline model baseline_meter_data, warnings = eemeter.get_baseline_data( daily_meter_data, end=install_start, max_days=365) # create a design matrix (the input to the model fitting step) baseline_design_matrix = eemeter.create_caltrack_daily_design_matrix( baseline_meter_data, temperature, ) # build a daily CalTRACK model baseline_model = eemeter.fit_caltrack_usage_per_day_model( baseline_design_matrix) # get a year of reporting period data reporting_meter_data, warnings = eemeter.get_reporting_data( daily_meter_data, start=install_end, max_days=365) # compute metered savings for the year of the reporting period we've selected metered_growth_dataframe, error_bands = eemeter.metered_savings( baseline_model, reporting_meter_data, temperature, with_disaggregated=True) # change signs for load growth metered_growth_dataframe['metered_savings'] = metered_growth_dataframe[ 'metered_savings'].apply(lambda x: x * -1) # total metered savings additional_load = metered_growth_dataframe.metered_savings.sum() # metrics metrics_raw = baseline_model.json() r_squared_adj = metrics_raw['r_squared_adj'] cvrmse_adj = metrics_raw['avgs_metrics']['cvrmse_adj'] metrics = [r_squared_adj, cvrmse_adj, additional_load] return metered_growth_dataframe, metrics, baseline_model
def test_json_daily(): meter_data, temperature_data, sample_metadata = ( eemeter.load_sample("il-electricity-cdd-hdd-daily")) blackout_start_date = sample_metadata["blackout_start_date"] blackout_end_date = sample_metadata["blackout_end_date"] # get meter data suitable for fitting a baseline model baseline_meter_data, warnings = eemeter.get_baseline_data( meter_data, end=blackout_start_date, max_days=365) # create a design matrix (the input to the model fitting step) baseline_design_matrix = eemeter.create_caltrack_daily_design_matrix( baseline_meter_data, temperature_data, ) # build a CalTRACK model baseline_model = eemeter.fit_caltrack_usage_per_day_model( baseline_design_matrix, ) # get a year of reporting period data reporting_meter_data, warnings = eemeter.get_reporting_data( meter_data, start=blackout_end_date, max_days=365) # compute metered savings metered_savings_dataframe, error_bands = eemeter.metered_savings( baseline_model, reporting_meter_data, temperature_data, with_disaggregated=True) # total metered savings total_metered_savings = metered_savings_dataframe.metered_savings.sum() # test JSON json_str = json.dumps(baseline_model.json()) m = eemeter.CalTRACKUsagePerDayModelResults.from_json(json.loads(json_str)) # compute metered savings from the loaded model metered_savings_dataframe, error_bands = eemeter.metered_savings( m, reporting_meter_data, temperature_data, with_disaggregated=True) # total metered savings total_metered_savings_2 = metered_savings_dataframe.metered_savings.sum() assert total_metered_savings == total_metered_savings_2
def read_sample_data(meter_data, temperature_data, sample_metadata): # the dates if an analysis "blackout" period during which a project was performed. blackout_start_date = sample_metadata["blackout_start_date"] blackout_end_date = sample_metadata["blackout_end_date"] # get meter data suitable for fitting a baseline model baseline_meter_data, warnings = eemeter.get_baseline_data( meter_data, end=blackout_start_date, max_days=365) # force alignment of weather data to the read meter data start = meter_data.iloc[0].name.to_pydatetime() end = meter_data.iloc[-1].name.to_pydatetime() return { 'meter_uom_id': 'energy_kWh', 'meter_data': meter_data, 'temperature_data': temperature_data, 'sample_metadata': sample_metadata, 'blackout_start_date': blackout_start_date, 'blackout_end_date': blackout_end_date, 'baseline_meter_data': baseline_meter_data, 'start': start, 'end': end }
def read_meter_data(meter, blackout_start_date=None, blackout_end_date=None, freq=None, start=None, end=None, uom=None): # get the meter data from the meter history logger.info('read_meter_data: freq %s', freq) out = StringIO() # read the meter data, returns the unit of the meter data m_uom = meter.write_meter_data_csv(out, columns=[{ 'as_of_datetime': 'start' }, { 'value': 'value' }], start=start, end=end) out.seek(0) if freq not in ("hourly", "daily"): freq = None meter_data = eeio.meter_data_from_csv(out, freq=freq) logger.info('read_meter_data: meter_data %s', meter_data) # force alignment of weather data to the read meter data start = meter_data.iloc[0].name.to_pydatetime() end = meter_data.iloc[-1].name.to_pydatetime() logger.info('read_meter_data: meter_data from %s to %s', start, end) # get the temperature data from the meter linked weather stations out = StringIO() meter.write_weather_data_csv(out, columns=[{ 'as_of_datetime': 'dt' }, { 'temp_f': 'tempF' }], start=start, end=end) out.seek(0) temperature_data = eeio.temperature_data_from_csv(out, freq="hourly") logger.info('read_meter_data: temperature_data %s', temperature_data) # we end the model on the given blackout_start_date else end it on the last data blm_end = blackout_start_date or end logger.info('read_meter_data: getting baseline_meter_data ending %s', blm_end) # get meter data suitable for fitting a baseline model baseline_meter_data, warnings = eemeter.get_baseline_data(meter_data, end=blm_end, max_days=365) logger.info('read_meter_data: baseline_meter_data %s', baseline_meter_data) start = baseline_meter_data.iloc[0].name.to_pydatetime() end = baseline_meter_data.iloc[-1].name.to_pydatetime() logger.info('read_meter_data: baseline_meter_data from %s to %s', start, end) logger.info('read_meter_data: DONE') return { 'meter_uom_id': m_uom.uom_id if m_uom else None, 'meter_data': meter_data, 'temperature_data': temperature_data, 'blackout_start_date': blackout_start_date, 'blackout_end_date': blackout_end_date, 'baseline_meter_data': baseline_meter_data, 'start': start, 'end': end }
def test_json_hourly(): meter_data, temperature_data, sample_metadata = ( eemeter.load_sample("il-electricity-cdd-hdd-hourly")) blackout_start_date = sample_metadata["blackout_start_date"] blackout_end_date = sample_metadata["blackout_end_date"] # get meter data suitable for fitting a baseline model baseline_meter_data, warnings = eemeter.get_baseline_data( meter_data, end=blackout_start_date, max_days=365) # create a design matrix for occupancy and segmentation preliminary_design_matrix = ( eemeter.create_caltrack_hourly_preliminary_design_matrix( baseline_meter_data, temperature_data, )) # build 12 monthly models - each step from now on operates on each segment segmentation = eemeter.segment_time_series(preliminary_design_matrix.index, 'three_month_weighted') # assign an occupancy status to each hour of the week (0-167) occupancy_lookup = eemeter.estimate_hour_of_week_occupancy( preliminary_design_matrix, segmentation=segmentation, ) # assign temperatures to bins temperature_bins = eemeter.fit_temperature_bins( preliminary_design_matrix, segmentation=segmentation, ) # build a design matrix for each monthly segment segmented_design_matrices = ( eemeter.create_caltrack_hourly_segmented_design_matrices( preliminary_design_matrix, segmentation, occupancy_lookup, temperature_bins, )) # build a CalTRACK hourly model baseline_model = eemeter.fit_caltrack_hourly_model( segmented_design_matrices, occupancy_lookup, temperature_bins, ) # get a year of reporting period data reporting_meter_data, warnings = eemeter.get_reporting_data( meter_data, start=blackout_end_date, max_days=365) # compute metered savings metered_savings_dataframe, error_bands = eemeter.metered_savings( baseline_model, reporting_meter_data, temperature_data, with_disaggregated=True) # total metered savings total_metered_savings = metered_savings_dataframe.metered_savings.sum() # test JSON json_str = json.dumps(baseline_model.json()) m = eemeter.CalTRACKHourlyModelResults.from_json(json.loads(json_str)) # compute metered savings from the loaded model metered_savings_dataframe, error_bands = eemeter.metered_savings( m, reporting_meter_data, temperature_data, with_disaggregated=True) # total metered savings total_metered_savings_2 = metered_savings_dataframe.metered_savings.sum() assert total_metered_savings == total_metered_savings_2
def eemeter_baseline_ami(file, temperature, install_start, install_end): """ This method uses linear regression to create hourly load profile to serve as the counterfactual for the period where the new measure has been installed. The two key parts of this model are the temperature and occupancy binning. CalTRACK refers to a standardized model used in california to measure energy efficiency savings from various measures (i.e. LED lightbulbs, efficient appliances, weatherization, etc.). In this case it will be used to model energy growth instead of savings. """ ami_data = eemeter.meter_data_from_csv(file, freq='hourly') # get meter data suitable for fitting a baseline model baseline_meter_data, warnings = eemeter.get_baseline_data( ami_data, end=install_start, max_days=365) # create design matrix for occupancy and segmentation preliminary_design_matrix = ( eemeter.create_caltrack_hourly_preliminary_design_matrix( baseline_meter_data, temperature, )) # build matrix with weights for monthly models of: # 0.5 = prior month # 1.0 = current month # 0.5 = post month segmentation = eemeter.segment_time_series( preliminary_design_matrix.index, 'three_month_weighted' # using 3 month weighted approach ) # assign an occupancy status to each hour of the week (0-167) occupancy_lookup = eemeter.estimate_hour_of_week_occupancy( preliminary_design_matrix, segmentation=segmentation, ) # assign temperatures to bins temperature_bins = eemeter.fit_temperature_bins( preliminary_design_matrix, segmentation=segmentation, ) # build a desgin matrix for each monthly segment segmented_design_matrices = ( eemeter.create_caltrack_hourly_segmented_design_matrices( preliminary_design_matrix, segmentation, occupancy_lookup, temperature_bins, )) # build a CalTRACK hourly model baseline_model = eemeter.fit_caltrack_hourly_model( segmented_design_matrices, occupancy_lookup, temperature_bins) # get a year of post installation reporting data reporting_meter_data, warnings = eemeter.get_reporting_data( ami_data, start=install_end, max_days=365) # compute metered load growth for the year of reporitng period metered_growth_dataframe, error_bands = eemeter.metered_savings( baseline_model, reporting_meter_data, temperature, with_disaggregated=True) metered_growth_dataframe['temp'] = temperature # append temperature # change signs for load growth metered_growth_dataframe['metered_savings'] = metered_growth_dataframe[ 'metered_savings'].apply(lambda x: x * -1) # totaled load growth additional_load = metered_growth_dataframe.metered_savings.sum() # metrics r_squared_adj_list = [] cvrmse_adj_list = [] # results in a dict model_results = list(baseline_model.json().values()) results = model_results[6] for segment, measures in results.items(): for measure, value in measures.items(): if measure == 'r_squared_adj': r_squared_adj_list.append(value) if measure == 'cvrmse_adj': cvrmse_adj_list.append(value) r_squared_adj = mean(r_squared_adj_list) cvrmse_adj = mean(cvrmse_adj_list) metrics = [r_squared_adj, cvrmse_adj, additional_load] # Return Section return metered_growth_dataframe, metrics, baseline_model