def find_best_annualized_usage_params(target_annualized_usage, model,
start_params, params_to_change, weather_normal_source, n_guesses=100):
best_params = start_params
meter = AnnualizedUsageMeter(model=model, temperature_unit_str=TEMPERATURE_UNIT_STR)
best_result = meter.evaluate_raw(model_params=best_params, weather_normal_source=weather_normal_source)
best_ann_usage = best_result["annualized_usage"][0]
for n in range(n_guesses):
resolution = abs((target_annualized_usage - best_ann_usage) / target_annualized_usage)
param_dict = best_params.to_dict()
for param_name,scale_factor in params_to_change:
current_value = param_dict[param_name]
current_value = norm.rvs(param_dict[param_name], resolution * scale_factor)
while current_value < 0:
current_value = norm.rvs(param_dict[param_name], resolution * scale_factor)
param_dict[param_name] = current_value
model_params = model.param_type(param_dict)
result = meter.evaluate_raw(model_params=model_params, weather_normal_source=weather_normal_source)
ann_usage = result["annualized_usage"][0]
if abs(target_annualized_usage - ann_usage) < abs(target_annualized_usage - best_ann_usage):
diff = abs(target_annualized_usage - best_ann_usage)
best_params = model_params
best_ann_usage = ann_usage
return best_params, best_ann_usage
def _generate_fuel_consumptions(self, weather_source,
weather_normal_source, period, model, param_dists,
param_delta_dists, noise, baseline_period, reporting_period,
fuel_type, consumption_unit_name, temperature_unit_name):
baseline_params = model.param_type([param.rvs() for param in param_dists.to_list()])
reporting_params = model.param_type([bl_param + param_delta.rvs()
for bl_param, param_delta in zip(baseline_params.to_list(), param_delta_dists.to_list())])
annualized_usage_meter = AnnualizedUsageMeter(temperature_unit_name,
model)
baseline_annualized_usage = annualized_usage_meter.evaluate_raw(
model_params=baseline_params,
weather_normal_source=weather_normal_source)["annualized_usage"]
reporting_annualized_usage = annualized_usage_meter.evaluate_raw(
model_params=reporting_params,
weather_normal_source=weather_normal_source)["annualized_usage"]
estimated_annualized_savings = baseline_annualized_usage - \
reporting_annualized_usage
baseline_generator = MonthlyBillingConsumptionGenerator(fuel_type,
consumption_unit_name, temperature_unit_name, model,
baseline_params)
reporting_generator = MonthlyBillingConsumptionGenerator(fuel_type,
consumption_unit_name, temperature_unit_name, model,
reporting_params)
datetimes = generate_monthly_billing_datetimes(period, dist=None)
baseline_consumption_data = baseline_generator.generate(
weather_source, datetimes, daily_noise_dist=noise)
reporting_consumption_data = reporting_generator.generate(
weather_source, datetimes, daily_noise_dist=noise)
baseline_data = baseline_consumption_data.data
reporting_data = reporting_consumption_data.data
periods = baseline_consumption_data.periods()
records = []
for bl_data, rp_data, period in zip(baseline_data, reporting_data,
periods):
if period in reporting_period:
val = rp_data
else:
val = bl_data
record = {"start": period.start, "end": period.end, "value": val}
records.append(record)
cd = ConsumptionData(records, fuel_type, consumption_unit_name,
record_type="arbitrary")
return cd, estimated_annualized_savings, baseline_params, \
reporting_params
def find_best_annualized_usage_params(target_annualized_usage,
model,
start_params,
params_to_change,
weather_normal_source,
n_guesses=100):
best_params = start_params
meter = AnnualizedUsageMeter(model=model,
temperature_unit_str=TEMPERATURE_UNIT_STR)
best_result = meter.evaluate_raw(
model_params=best_params, weather_normal_source=weather_normal_source)
best_ann_usage = best_result["annualized_usage"][0]
for n in range(n_guesses):
resolution = abs((target_annualized_usage - best_ann_usage) /
target_annualized_usage)
param_dict = best_params.to_dict()
for param_name, scale_factor in params_to_change:
current_value = param_dict[param_name]
current_value = norm.rvs(param_dict[param_name],
resolution * scale_factor)
while current_value < 0:
current_value = norm.rvs(param_dict[param_name],
resolution * scale_factor)
param_dict[param_name] = current_value
model_params = model.param_type(param_dict)
result = meter.evaluate_raw(
model_params=model_params,
weather_normal_source=weather_normal_source)
ann_usage = result["annualized_usage"][0]
if abs(target_annualized_usage -
ann_usage) < abs(target_annualized_usage - best_ann_usage):
diff = abs(target_annualized_usage - best_ann_usage)
best_params = model_params
best_ann_usage = ann_usage
return best_params, best_ann_usage