def create_caltrack_hourly_preliminary_design_matrix(meter_data, temperature_data):
"""A helper function which calls basic feature creation methods to create an
input suitable for use in the first step of creating a CalTRACK hourly model.
Parameters
----------
meter_data : :any:`pandas.DataFrame`
Hourly meter data in eemeter format.
temperature_data : :any:`pandas.Series`
Hourly temperature data in eemeter format.
Returns
-------
design_matrix : :any:`pandas.DataFrame`
A design matrix with meter_value, hour_of_week, hdd_50, and cdd_65 features.
"""
time_features = compute_time_features(
meter_data.index, hour_of_week=True, hour_of_day=False, day_of_week=False
)
temperature_features = compute_temperature_features(
meter_data.index,
temperature_data,
heating_balance_points=[50],
cooling_balance_points=[65],
degree_day_method="hourly",
)
design_matrix = merge_features(
[meter_data.value.to_frame("meter_value"), temperature_features, time_features]
)
return design_matrix
def create_caltrack_billing_design_matrix(meter_data, temperature_data):
"""A helper function which calls basic feature creation methods to create a
design matrix suitable for use with CalTRACK Billing methods.
Parameters
----------
meter_data : :any:`pandas.DataFrame`
Hourly meter data in eemeter format.
temperature_data : :any:`pandas.Series`
Hourly temperature data in eemeter format.
Returns
-------
design_matrix : :any:`pandas.DataFrame`
A design matrics with mean usage_per_day, hdd_30-hdd_90, and cdd_30-cdd_90
features.
"""
usage_per_day = compute_usage_per_day_feature(meter_data, series_name="meter_value")
temperature_features = compute_temperature_features(
meter_data.index,
temperature_data,
heating_balance_points=range(30, 91),
cooling_balance_points=range(30, 91),
data_quality=True,
tolerance=pd.Timedelta(
"35D"
), # limit temperature data matching to periods of up to 35 days.
)
design_matrix = merge_features([usage_per_day, temperature_features])
return design_matrix
def create_caltrack_daily_design_matrix(meter_data, temperature_data):
usage_per_day = compute_usage_per_day_feature(meter_data,
series_name="meter_value")
temperature_features = compute_temperature_features(
meter_data.index,
temperature_data,
heating_balance_points=range(30, 91),
cooling_balance_points=range(30, 91),
data_quality=True,
)
design_matrix = merge_features([usage_per_day, temperature_features])
return design_matrix
def occupancy_precursor(il_electricity_cdd_hdd_hourly):
meter_data = il_electricity_cdd_hdd_hourly["meter_data"]
temperature_data = il_electricity_cdd_hdd_hourly["temperature_data"]
time_features = compute_time_features(meter_data.index)
temperature_features = compute_temperature_features(
meter_data.index,
temperature_data,
heating_balance_points=[50],
cooling_balance_points=[65],
degree_day_method="hourly",
)
return merge_features([
meter_data.value.to_frame("meter_value"), temperature_features,
time_features
])
def create_caltrack_billing_design_matrix(meter_data, temperature_data):
usage_per_day = compute_usage_per_day_feature(meter_data,
series_name="meter_value")
temperature_features = compute_temperature_features(
meter_data.index,
temperature_data,
heating_balance_points=range(30, 91),
cooling_balance_points=range(30, 91),
data_quality=True,
tolerance=pd.Timedelta(
"35D"
), # limit temperature data matching to periods of up to 35 days.
)
design_matrix = merge_features([usage_per_day, temperature_features])
return design_matrix
def occupancy_precursor_only_nan(il_electricity_cdd_hdd_hourly):
meter_data = il_electricity_cdd_hdd_hourly["meter_data"]
meter_data = meter_data[datetime(2017, 1, 4):datetime(2017, 6, 1)]
meter_data.iloc[-1] = np.nan
# Simulates a segment where there is only a single nan value
temperature_data = il_electricity_cdd_hdd_hourly["temperature_data"]
time_features = compute_time_features(meter_data.index)
temperature_features = compute_temperature_features(
meter_data.index,
temperature_data,
heating_balance_points=[50],
cooling_balance_points=[65],
degree_day_method="hourly",
)
return merge_features([
meter_data.value.to_frame("meter_value"), temperature_features,
time_features
])
def create_caltrack_hourly_preliminary_design_matrix(meter_data,
temperature_data):
time_features = compute_time_features(meter_data.index,
hour_of_week=True,
hour_of_day=False,
day_of_week=False)
temperature_features = compute_temperature_features(
meter_data.index,
temperature_data,
heating_balance_points=[50],
cooling_balance_points=[65],
degree_day_method="hourly",
)
design_matrix = merge_features([
meter_data.value.to_frame("meter_value"), temperature_features,
time_features
])
return design_matrix
def test_merge_features():
index = pd.date_range("2017-01-01", periods=100, freq="H", tz="UTC")
features = merge_features([
pd.Series(1, index=index, name="a"),
pd.DataFrame({"b": 2}, index=index),
pd.DataFrame({
"c": 3,
"d": 4
}, index=index),
])
assert list(features.columns) == ["a", "b", "c", "d"]
assert features.shape == (100, 4)
assert features.sum().sum() == 1000
assert features.a.sum() == 100
assert features.b.sum() == 200
assert features.c.sum() == 300
assert features.d.sum() == 400
assert features.index[0] == index[0]
assert features.index[-1] == index[-1]
def test_merge_features_empty_raises():
with pytest.raises(ValueError):
features = merge_features([])
