def test_model_weather_input_not_np_array():
model = AverageDailyTemperatureSensitivityModel(heating=False,
cooling=False)
params = model.param_type([1])
observed_temps = [[70], [65, 60]]
usages = model.transform(observed_temps, params)
assert_allclose(usages, [1, 1], rtol=1e-2, atol=1e-2)
def test_average_daily_baseload_heating_cooling_consumption_model():
initial_params = {
"base_daily_consumption": 0,
"heating_slope": 0,
"cooling_slope": 0,
"heating_balance_temperature": 55,
"cooling_balance_temperature": 57,
}
param_bounds = {
"base_daily_consumption": [0, 100],
"heating_slope": [0, 100],
"cooling_slope": [0, 100],
"heating_balance_temperature": [50, 60],
"cooling_balance_temperature": [52, 72],
}
model = AverageDailyTemperatureSensitivityModel(
cooling=True,
heating=True,
initial_params=initial_params,
param_bounds=param_bounds)
params = model.param_type([1, 60, 1, 65, 1])
observed_temps = np.array([[i] for i in range(50, 70)])
usages = model.transform(observed_temps, params)
assert_allclose(usages[8:18], [3, 2, 1, 1, 1, 1, 1, 1, 2, 3],
rtol=1e-2,
atol=1e-2)
opt_params = model.fit(observed_temps, usages)
assert_allclose(params.to_list(),
opt_params.to_list(),
rtol=1e-2,
atol=1e-2)
def consumption_generator_2(request):
model_params, fuel_type, consumption_unit_name, temperature_unit_name = \
request.param
model = AverageDailyTemperatureSensitivityModel(cooling=True, heating=True)
params = {
"base_daily_consumption": model_params[0],
"heating_slope": model_params[1],
"heating_balance_temperature": model_params[2],
"cooling_slope": model_params[3],
"cooling_balance_temperature": model_params[4]
}
generator = MonthlyBillingConsumptionGenerator(fuel_type,
consumption_unit_name, temperature_unit_name, model, params)
params = model.param_type(params)
return generator, params
def test_TemperatureSensitivityModel_with_cooling():
initial_params = {
"base_daily_consumption": 0,
"cooling_slope": 0,
"cooling_balance_temperature": 57,
}
param_bounds = {
"base_daily_consumption": [0,100],
"cooling_slope": [0,100],
"cooling_balance_temperature": [52,72],
}
model = AverageDailyTemperatureSensitivityModel(heating=False,cooling=True,initial_params=initial_params,param_bounds=param_bounds)
params = model.param_type([1,60,1])
observed_temps = np.array([[i] for i in range(50,70)])
usages = model.transform(observed_temps, params)
assert_allclose(usages[8:13],[1,1,1,2,3])
opt_params = model.fit(observed_temps, usages)
assert_allclose(params.to_list(), opt_params.to_list(), rtol=1e-2, atol=1e-2)
def default_residential_outputs_1(request, gsod_722880_2012_2014_weather_source):
elec_model_params, gas_model_params, \
elec_annualized_usage, gas_annualized_usage, \
elec_gross_savings, gas_gross_savings, \
elec_rmse, gas_rmse, elec_r_squared, gas_r_squared, \
temp_unit, cdd_tmy, hdd_tmy, total_cdd, total_hdd \
= request.param
period = Period(datetime(2012,1,1,tzinfo=pytz.utc),
datetime(2014,12,31,tzinfo=pytz.utc))
retrofit_start_date = datetime(2013,6,1,tzinfo=pytz.utc)
retrofit_completion_date = datetime(2013,8,1,tzinfo=pytz.utc)
datetimes = generate_monthly_billing_datetimes(period,randint(30,31))
# generate electricity consumption
elec_model = AverageDailyTemperatureSensitivityModel(cooling=True,heating=True)
elec_params = {
"base_daily_consumption": elec_model_params[0],
"heating_balance_temperature": elec_model_params[1],
"heating_slope": elec_model_params[2],
"cooling_balance_temperature": elec_model_params[3],
"cooling_slope": elec_model_params[4],
}
elec_gen = MonthlyBillingConsumptionGenerator("electricity", "kWh", temp_unit,
elec_model, elec_params)
elec_consumption_data = elec_gen.generate(gsod_722880_2012_2014_weather_source, datetimes)
elec_consumption_kWh_per_day, elec_consumption_n_days = \
elec_consumption_data.average_daily_consumptions()
elec_params = elec_model.param_type(elec_params)
# generate natural_gas consumption
gas_model = AverageDailyTemperatureSensitivityModel(cooling=False,heating=True)
gas_params = {
"base_daily_consumption": gas_model_params[0],
"heating_balance_temperature": gas_model_params[1],
"heating_slope": gas_model_params[2],
}
gas_gen = MonthlyBillingConsumptionGenerator("natural_gas", "therm", temp_unit,
gas_model, gas_params)
gas_consumption_data = gas_gen.generate(gsod_722880_2012_2014_weather_source, datetimes)
gas_consumption_kWh_per_day, gas_consumption_n_days = \
gas_consumption_data.average_daily_consumptions()
gas_params = gas_model.param_type(gas_params)
fixture = elec_consumption_data, gas_consumption_data, \
elec_params, gas_params, \
elec_annualized_usage, gas_annualized_usage, \
elec_gross_savings, gas_gross_savings, \
elec_rmse, gas_rmse, \
elec_r_squared, gas_r_squared, \
elec_consumption_kWh_per_day, gas_consumption_kWh_per_day, \
elec_consumption_n_days, gas_consumption_n_days, \
temp_unit, retrofit_start_date, retrofit_completion_date, \
cdd_tmy, hdd_tmy, total_cdd, total_hdd
return fixture
def generated_consumption_data_with_n_periods_cdd_1(request,
gsod_722880_2012_2014_weather_source):
model_params, period, n_periods_1, n_periods_2, n_periods_3 = request.param
model = AverageDailyTemperatureSensitivityModel(cooling=True,heating=True)
params = {
"base_daily_consumption": model_params[0],
"heating_slope": model_params[1],
"heating_balance_temperature": model_params[2],
"cooling_slope": model_params[3],
"cooling_balance_temperature": model_params[4]
}
gen = MonthlyBillingConsumptionGenerator("electricity", "kWh", "degF",
model, params)
datetimes = generate_monthly_billing_datetimes(period, dist=randint(30,31))
consumption_data = gen.generate(gsod_722880_2012_2014_weather_source,
datetimes)
return consumption_data, n_periods_1, n_periods_2, n_periods_3
def test_gross_savings_metric(
generated_consumption_data_pre_post_with_gross_savings_1,
gsod_722880_2012_2014_weather_source):
model = AverageDailyTemperatureSensitivityModel(heating=True, cooling=True)
meter = GrossSavingsMeter(temperature_unit_str="degF", model=model)
cd, params_pre, params_post, retrofit_date, savings = \
generated_consumption_data_pre_post_with_gross_savings_1
reporting_period = Period(retrofit_date, datetime(2015, 1, 1))
result = meter.evaluate_raw(
consumption_data_reporting=cd.filter_by_period(reporting_period),
model_params_baseline=params_pre,
weather_source=gsod_722880_2012_2014_weather_source,
energy_unit_str="kWh")
assert_allclose(result["gross_savings"], savings, rtol=RTOL, atol=ATOL)
df_t = pd.Series(formattedWeatherData.data)
df_t.index = df_t.index.to_datetime()
df_t = pd.DataFrame(df_t)
df_t.columns = ["TempC"]
model = AverageDailyTemperatureSensitivityModel(
heating=True,
cooling=True,
initial_params={
"base_daily_consumption": 0,
"heating_slope": 0,
"heating_balance_temperature": 10,
"cooling_slope": 0,
"cooling_balance_temperature": 20,
},
param_bounds={
"base_daily_consumption": [0,1000000],
"heating_slope": [0,1000],
"heating_balance_temperature": [5,16],
"cooling_slope": [0,1000],
"cooling_balance_temperature": [13,24],
})
# This weather source base !!
#ThisWeatherData= WeatherSourceBase()
ExtractedData_chosenBuilding = pd.DataFrame(ExtractedData_chosenBuilding)
to_load = ExtractedData_chosenBuilding.resample('D').sum().tz_localize(None)
def get_example_project(zipcode):
# location
location = Location(zipcode=zipcode)
station = location.station
weather_source = GSODWeatherSource(station,2011,2015)
# model
model_e = AverageDailyTemperatureSensitivityModel(cooling=True, heating=True)
model_g = AverageDailyTemperatureSensitivityModel(cooling=False, heating=True)
# model params
params_e_b = {
"cooling_slope": 1,
"heating_slope": 1,
"base_daily_consumption": 30,
"cooling_balance_temperature": 73,
"heating_balance_temperature": 68,
}
params_e_r = {
"cooling_slope": .5,
"heating_slope": .5,
"base_daily_consumption": 15,
"cooling_balance_temperature": 73,
"heating_balance_temperature": 68,
}
params_g_b = {
"heating_slope": .2,
"base_daily_consumption": 2,
"heating_balance_temperature": 68,
}
params_g_r = {
"heating_slope": .1,
"base_daily_consumption": 1,
"heating_balance_temperature": 68,
}
#generators
gen_e_b = MonthlyBillingConsumptionGenerator("electricity", "kWh", "degF",
model_e, params_e_b)
gen_e_r = MonthlyBillingConsumptionGenerator("electricity", "kWh", "degF",
model_e, params_e_r)
gen_g_b = MonthlyBillingConsumptionGenerator("natural_gas", "therm", "degF",
model_g, params_g_b)
gen_g_r = MonthlyBillingConsumptionGenerator("natural_gas", "therm", "degF",
model_g, params_g_r)
# time periods
period = Period(datetime(2011,1,1,tzinfo=pytz.utc), datetime(2015,1,1,tzinfo=pytz.utc))
datetimes = generate_monthly_billing_datetimes(period, dist=randint(30,31))
# consumption data
cd_e_b = gen_e_b.generate(weather_source, datetimes, daily_noise_dist=None)
cd_e_r = gen_e_r.generate(weather_source, datetimes, daily_noise_dist=None)
cd_g_b = gen_g_b.generate(weather_source, datetimes, daily_noise_dist=None)
cd_g_r = gen_g_r.generate(weather_source, datetimes, daily_noise_dist=None)
# periods
periods = cd_e_b.periods()
reporting_period = Period(datetime(2013,1,1,tzinfo=pytz.utc), datetime(2015,1,1,tzinfo=pytz.utc))
baseline_period = Period(datetime(2011,1,1,tzinfo=pytz.utc), datetime(2013,1,1,tzinfo=pytz.utc))
# records
records_e = []
records_g = []
for e_b, e_r, g_b, g_r, p in zip(cd_e_b.data, cd_e_r.data, cd_g_b.data, cd_g_r.data, periods):
e = e_r if p in reporting_period else e_b
g = g_r if p in reporting_period else g_b
record_e = {"start": p.start, "end": p.end, "value": e}
record_g = {"start": p.start, "end": p.end, "value": g}
records_e.append(record_e)
records_g.append(record_g)
# consumption_data
cd_e = ConsumptionData(records_e, "electricity", "kWh",
record_type="arbitrary")
cd_g = ConsumptionData(records_g, "natural_gas", "therm",
record_type="arbitrary")
consumptions = [cd_e, cd_g]
# project
project = Project(location, consumptions, baseline_period, reporting_period)
return project
def test_model_weather_input_not_np_array():
model = AverageDailyTemperatureSensitivityModel(heating=False,cooling=False)
params = model.param_type([1])
observed_temps = [[70],[65,60]]
usages = model.transform(observed_temps, params)
assert_allclose(usages, [1,1], rtol=1e-2, atol=1e-2)
