def get_daily_model(data,
fit_cdd=True,
fit_intercept_only=True,
fit_cdd_only=True,
fit_hdd_only=True,
fit_cdd_hdd=True):
logger.info('get_daily_model: ...')
# create a design matrix (the input to the model fitting step)
logger.info('get_daily_model: creating baseline_design_matrix ...')
baseline_design_matrix = eemeter.create_caltrack_daily_design_matrix(
data['baseline_meter_data'],
data['temperature_data'],
)
# build a CalTRACK model
logger.info('get_daily_model: building CalTRACK model ...')
baseline_model = eemeter.fit_caltrack_usage_per_day_model(
baseline_design_matrix,
fit_cdd=fit_cdd,
fit_intercept_only=fit_intercept_only,
fit_cdd_only=fit_cdd_only,
fit_hdd_only=fit_hdd_only,
fit_cdd_hdd=fit_cdd_hdd)
logger.info('get_daily_model: DONE')
return baseline_model
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 get_daily_model(data, **kwargs):
logger.info('get_daily_model: ...')
# create a design matrix (the input to the model fitting step)
logger.info('get_daily_model: creating baseline_design_matrix ...')
baseline_design_matrix = eemeter.create_caltrack_daily_design_matrix(
data['baseline_meter_data'],
data['temperature_data'],
)
# build a CalTRACK model
logger.info('get_daily_model: building CalTRACK model ...')
baseline_model = eemeter.fit_caltrack_usage_per_day_model(
baseline_design_matrix, **kwargs)
logger.info('get_daily_model: DONE')
return baseline_model