import pandas as pd
import numpy as np
from scipy.optimize import leastsq
from datetime import datetime, timedelta
from collections import namedtuple
from thermostat.regression import runtime_regression
from thermostat import get_version
from pkg_resources import resource_stream
import inspect
CoreDaySet = namedtuple("CoreDaySet",
["name", "daily", "hourly", "start_date", "end_date"]
)
[docs]class Thermostat(object):
""" Main thermostat data container. Each parameter which contains
timeseries data should be a pandas.Series with a datetimeIndex, and that
each index should be equivalent.
Parameters
----------
thermostat_id : object
An identifier for the thermostat. Can be anything, but should be
identifying (e.g., an ID provided by the manufacturer).
equipment_type : { 0, 1, 2, 3, 4, 5 }
- :code:`0`: Other - e.g. multi-zone multi-stage, modulating. Note: module will
not output savings data for this type.
- :code:`1`: Single stage heat pump with aux and/or emergency heat
- :code:`2`: Single stage heat pump without aux or emergency heat
- :code:`3`: Single stage non heat pump with single-stage central air conditioning
- :code:`4`: Single stage non heat pump without central air conditioning
- :code:`5`: Single stage central air conditioning without central heating
zipcode : str
Installation ZIP code for the thermostat.
station : str
USAF identifier for weather station used to pull outdoor temperature data.
temperature_in : pandas.Series
Contains internal temperature data in degrees Fahrenheit (F),
with resolution of at least 0.5F.
Should be indexed by a pandas.DatetimeIndex with hourly frequency (i.e.
:code:`freq='H'`).
heating_setpoint : pandas.Series
Contains target temperature (setpoint) data in degrees Fahrenheit (F),
with resolution of at least 0.5F used to control heating equipment.
Should be indexed by a pandas.DatetimeIndex with hourly frequency (i.e.
:code:`freq='H'`).
cooling_setpoint : pandas.Series
Contains target temperature (setpoint) data in degrees Fahrenheit (F),
with resolution of at least 0.5F used to control cooling equipment.
Should be indexed by a pandas.DatetimeIndex with hourly frequency (i.e.
:code:`freq='H'`).
temperature_out : pandas.Series
Contains outdoor temperature (setpoint) data as observed by a relevant
weather station in degrees Fahrenheit (F), with resolution of at least
0.5F.
Should be indexed by a pandas.DatetimeIndex with hourly frequency (i.e.
:code:`freq='H'`).
cool_runtime : pandas.Series,
Daily runtimes for cooling equipment controlled by the thermostat, measured
in minutes. No datapoint should exceed 1440 mins, which would indicate
over a day of runtime (impossible).
Should be indexed by a pandas.DatetimeIndex with daily frequency (i.e.
:code:`freq='D'`).
heat_runtime : pandas.Series,
Daily runtimes for heating equipment controlled by the thermostat, measured
in minutes. No datapoint should exceed 1440 mins, which would indicate
over a day of runtime (impossible).
Should be indexed by a pandas.DatetimeIndex with daily frequency (i.e.
:code:`freq='D'`).
auxiliary_heat_runtime : pandas.Series,
Hourly runtimes for auxiliary heating equipment controlled by the
thermostat, measured in minutes. Auxiliary heat runtime is counted when
both resistance heating and the compressor are running (for heat pump
systems). No datapoint should exceed 60 mins, which would indicate
over a hour of runtime (impossible).
Should be indexed by a pandas.DatetimeIndex with hourly frequency (i.e.
:code:`freq='H'`).
energency_heat_runtime : pandas.Series,
Hourly runtimes for emergency heating equipment controlled by the
thermostat, measured in minutes. Emergency heat runtime is counted when
resistance heating is running when the compressor is not (for heat pump
systems). No datapoint should exceed 60 mins, which would indicate
over a hour of runtime (impossible).
Should be indexed by a pandas.DatetimeIndex with hourly frequency (i.e.
:code:`freq='H'`).
"""
HEATING_EQUIPMENT_TYPES = set([1, 2, 3, 4])
COOLING_EQUIPMENT_TYPES = set([1, 2, 3, 5])
AUX_EMERG_EQUIPMENT_TYPES = set([1])
def __init__(
self, thermostat_id, equipment_type, zipcode, station,
temperature_in, temperature_out, cooling_setpoint,
heating_setpoint, cool_runtime, heat_runtime,
auxiliary_heat_runtime, emergency_heat_runtime):
self.thermostat_id = thermostat_id
self.equipment_type = equipment_type
self.zipcode = zipcode
self.station = station
self.temperature_in = self._interpolate(temperature_in, method="linear")
self.temperature_out = self._interpolate(temperature_out, method="linear")
self.cooling_setpoint = cooling_setpoint
self.heating_setpoint = heating_setpoint
self.cool_runtime = cool_runtime
self.heat_runtime = heat_runtime
self.auxiliary_heat_runtime = auxiliary_heat_runtime
self.emergency_heat_runtime = emergency_heat_runtime
self.validate()
def validate(self):
self._validate_heating()
self._validate_cooling()
self._validate_aux_emerg()
def _validate_heating(self):
if self.equipment_type in self.HEATING_EQUIPMENT_TYPES:
if self.heat_runtime is None:
message = "For thermostat {}, heating runtime data was not provided," \
" despite equipment type of {}, which requires heating data.".format(self.thermostat_id, self.equipment_type)
raise ValueError(message)
if self.heating_setpoint is None:
message = "For thermostat {}, heating setpoint data was not provided," \
" despite equipment type of {}, which requires heating data." \
" If only one setpoint is used, (or if there is no distinction" \
" between the heating and cooling setpoints, please" \
" explicitly provide two copies of the available setpoint data" \
.format(self.thermostat_id, self.equipment_type)
raise ValueError(message)
def _validate_cooling(self):
if self.equipment_type in self.COOLING_EQUIPMENT_TYPES:
if self.cool_runtime is None:
message = "For thermostat {}, cooling runtime data was not provided," \
" despite equipment type of {}, which requires cooling data.".format(self.thermostat_id, self.equipment_type)
raise ValueError(message)
if self.cooling_setpoint is None:
message = "For thermostat {}, cooling setpoint data was not provided," \
" despite equipment type of {}, which requires heating data." \
" If only one setpoint is used, (or if there is no distinction" \
" between the heating and cooling setpoints, please" \
" explicitly provide two copies of the available setpoint data" \
.format(self.thermostat_id, self.equipment_type)
raise ValueError(message)
def _validate_aux_emerg(self):
if self.equipment_type in self.AUX_EMERG_EQUIPMENT_TYPES:
if self.auxiliary_heat_runtime is None or self.emergency_heat_runtime is None:
message = "For thermostat {}, aux and emergency runtime data were not provided," \
" despite equipment type of {}, which requires these columns of data."\
" If none is available, please change to equipment_type 2," \
" or provide columns of 0s".format(self.thermostat_id, self.equipment_type)
raise ValueError(message)
def _interpolate(self, series, method="linear"):
if method not in ["linear"]:
return series
return series.interpolate(method="linear", limit=1, limit_direction="both")
def _protect_heating(self):
function_name = inspect.stack()[1][3]
if self.equipment_type not in self.HEATING_EQUIPMENT_TYPES:
message = "The function '{}', which is heating specific, cannot be" \
" called for equipment_type {}".format(function_name, self.equipment_type)
raise ValueError(message)
def _protect_cooling(self):
function_name = inspect.stack()[1][3]
if self.equipment_type not in self.COOLING_EQUIPMENT_TYPES:
message = "The function '{}', which is cooling specific, cannot be" \
" called for equipment_type {}".format(function_name, self.equipment_type)
raise ValueError(message)
def _protect_aux_emerg(self):
function_name = inspect.stack()[1][3]
if self.equipment_type not in self.AUX_EMERG_EQUIPMENT_TYPES:
message = "The function '{}', which is auxiliary/emergency heating specific, cannot be" \
" called for equipment_type {}".format(function_name, self.equipment_type)
raise ValueError(message)
[docs] def get_core_heating_days(self, method="entire_dataset",
min_minutes_heating=30, max_minutes_cooling=0):
""" Determine core heating days from data associated with this thermostat
Parameters
----------
method : {"entire_dataset", "year_mid_to_mid"}, default: "entire_dataset"
Method by which to find core heating day sets.
- "entire_dataset": all heating days in dataset (days with >= 30 min
of heating runtime and no cooling runtime. (default)
- "year_mid_to_mid": groups all heating days (days with >= 30 min
of total heating and no cooling) from July 1 to June 30
(inclusive) into individual core heating day sets. May overlap
with core cooling day sets.
min_minutes_heating : int, default 30
Number of minutes of heating runtime per day required for inclusion
in core heating day set.
max_minutes_cooling : int, default 0
Number of minutes of cooling runtime per day beyond which the day
is considered part of a shoulder season (and is therefore not part
of the core heating day set).
Returns
-------
core_heating_day_sets : list of thermostat.core.CoreDaySet objects
List of core day sets detected; Core day sets are represented as
pandas Series of boolean values, intended to be used as selectors
or masks on the thermostat data at hourly and daily frequencies.
A value of True at a particular index indicates inclusion of
of the data at that index in the core day set. If method is
"entire_dataset", name of core day sets are "heating_ALL"; if method
is "year_mid_to_mid", names of core day sets are of the form
"heating_YYYY-YYYY"
"""
if method not in ["year_mid_to_mid", "entire_dataset"]:
raise NotImplementedError
self._protect_heating()
# compute inclusion thresholds
meets_heating_thresholds = self.heat_runtime >= min_minutes_heating
if self.equipment_type in self.COOLING_EQUIPMENT_TYPES:
meets_cooling_thresholds = self.cool_runtime <= max_minutes_cooling
else:
meets_cooling_thresholds = True
meets_thresholds = meets_heating_thresholds & meets_cooling_thresholds
# enough temperature_in
enough_temp_in = \
self.temperature_in.groupby(self.temperature_in.index.date) \
.apply(lambda x: x.isnull().sum() <= 2)
enough_temp_out = \
self.temperature_out.groupby(self.temperature_out.index.date) \
.apply(lambda x: x.isnull().sum() <= 2)
meets_thresholds &= enough_temp_in & enough_temp_out
data_start_date = np.datetime64(self.heat_runtime.index[0])
data_end_date = np.datetime64(self.heat_runtime.index[-1])
if method == "year_mid_to_mid":
# find all potential core heating day ranges
start_year = data_start_date.item().year - 1
end_year = data_end_date.item().year + 1
potential_core_day_sets = zip(range(start_year, end_year),
range(start_year + 1, end_year + 1))
# for each potential core day set, look for core heating days.
core_heating_day_sets = []
for start_year_, end_year_ in potential_core_day_sets:
core_day_set_start_date = np.datetime64(datetime(start_year_, 7, 1))
core_day_set_end_date = np.datetime64(datetime(end_year_, 7, 1))
start_date = max(core_day_set_start_date, data_start_date).item()
end_date = min(core_day_set_end_date, data_end_date).item()
in_range = self._get_range_boolean(self.heat_runtime.index,
start_date, end_date)
inclusion_daily = pd.Series(in_range & meets_thresholds,
index=self.heat_runtime.index)
if any(inclusion_daily):
name = "heating_{}-{}".format(start_year_, end_year_)
inclusion_hourly = self._get_hourly_boolean(inclusion_daily)
core_day_set = CoreDaySet(name, inclusion_daily, inclusion_hourly,
start_date, end_date)
core_heating_day_sets.append(core_day_set)
return core_heating_day_sets
elif method == "entire_dataset":
inclusion_daily = pd.Series(meets_thresholds, index=self.heat_runtime.index)
inclusion_hourly = self._get_hourly_boolean(inclusion_daily)
core_heating_day_set = CoreDaySet(
"heating_ALL",
inclusion_daily,
inclusion_hourly,
data_start_date,
data_end_date)
# returned as list for consistency
core_heating_day_sets = [core_heating_day_set]
return core_heating_day_sets
[docs] def get_core_cooling_days(self, method="entire_dataset",
min_minutes_cooling=30, max_minutes_heating=0):
""" Determine core cooling days from data associated with this
thermostat.
Parameters
----------
method : {"entire_dataset", "year_end_to_end"}, default: "entire_dataset"
Method by which to find core cooling days.
- "entire_dataset": all cooling days in dataset (days with >= 30 min
of cooling runtime and no heating runtime.
- "year_end_to_end": groups all cooling days (days with >= 30 min
of total cooling and no heating) from January 1 to December 31
into individual core cooling sets.
min_minutes_cooling : int, default 30
Number of minutes of core cooling runtime per day required for
inclusion in core cooling day set.
max_minutes_heating : int, default 0
Number of minutes of heating runtime per day beyond which the day is
considered part of a shoulder season (and is therefore not part of
the core cooling day set).
Returns
-------
core_cooling_day_sets : list of thermostat.core.CoreDaySet objects
List of core day sets detected; Core day sets are represented as
pandas Series of boolean values, intended to be used as selectors
or masks on the thermostat data at hourly and daily frequencies.
A value of True at a particular index indicates inclusion of
of the data at that index in the core day set. If method is
"entire_dataset", name of core day set is "cooling_ALL"; if method
is "year_end_to_end", names of core day sets are of the form
"cooling_YYYY"
"""
if method not in ["year_end_to_end", "entire_dataset"]:
raise NotImplementedError
self._protect_cooling()
# find all potential core cooling day ranges
data_start_date = np.datetime64(self.cool_runtime.index[0])
data_end_date = np.datetime64(self.cool_runtime.index[-1])
# compute inclusion thresholds
if self.equipment_type in self.HEATING_EQUIPMENT_TYPES:
meets_heating_thresholds = self.heat_runtime <= max_minutes_heating
else:
meets_heating_thresholds = True
meets_cooling_thresholds = self.cool_runtime >= min_minutes_cooling
meets_thresholds = meets_heating_thresholds & meets_cooling_thresholds
# enough temperature_in
enough_temp_in = \
self.temperature_in.groupby(self.temperature_in.index.date) \
.apply(lambda x: x.isnull().sum() <= 2)
enough_temp_out = \
self.temperature_out.groupby(self.temperature_out.index.date) \
.apply(lambda x: x.isnull().sum() <= 2)
meets_thresholds &= enough_temp_in & enough_temp_out
if method == "year_end_to_end":
start_year = data_start_date.item().year
end_year = data_end_date.item().year
potential_core_day_sets = range(start_year, end_year + 1)
# for each potential core day set, look for cooling days.
core_cooling_day_sets = []
for year in potential_core_day_sets:
core_day_set_start_date = np.datetime64(datetime(year, 1, 1))
core_day_set_end_date = np.datetime64(datetime(year + 1, 1, 1))
start_date = max(core_day_set_start_date, data_start_date).item()
end_date = min(core_day_set_end_date, data_end_date).item()
in_range = self._get_range_boolean(self.cool_runtime.index,
start_date, end_date)
inclusion_daily = pd.Series(in_range & meets_thresholds,
index=self.cool_runtime.index)
if any(inclusion_daily):
name = "cooling_{}".format(year)
inclusion_hourly = self._get_hourly_boolean(inclusion_daily)
core_day_set = CoreDaySet(name, inclusion_daily, inclusion_hourly,
start_date, end_date)
core_cooling_day_sets.append(core_day_set)
return core_cooling_day_sets
elif method == "entire_dataset":
inclusion_daily = pd.Series(meets_thresholds, index=self.cool_runtime.index)
inclusion_hourly = self._get_hourly_boolean(inclusion_daily)
core_day_set = CoreDaySet(
"cooling_ALL",
inclusion_daily,
inclusion_hourly,
data_start_date,
data_end_date)
core_cooling_day_sets = [core_day_set]
return core_cooling_day_sets
def _get_range_boolean(self, dt_index, start_date, end_date):
after_start = dt_index >= start_date
before_end = dt_index < end_date
return after_start & before_end
def _get_hourly_boolean(self, daily_boolean):
values = np.repeat(daily_boolean.values, 24)
index = pd.DatetimeIndex(start=daily_boolean.index[0],
periods=daily_boolean.index.shape[0] * 24, freq="H")
hourly_boolean = pd.Series(values, index)
return hourly_boolean
[docs] def total_heating_runtime(self, core_day_set):
""" Calculates total heating runtime.
Parameters
----------
core_day_set : thermostat.core.CoreDaySet
Core day set for which to calculate total runtime.
Returns
-------
total_runtime : float
Total heating runtime.
"""
self._protect_heating()
return self.heat_runtime[core_day_set.daily].sum()
[docs] def total_auxiliary_heating_runtime(self, core_day_set):
""" Calculates total auxiliary heating runtime.
Parameters
----------
core_day_set : thermostat.core.CoreDaySet
Core day set for which to calculate total runtime.
Returns
-------
total_runtime : float
Total auxiliary heating runtime.
"""
self._protect_aux_emerg()
return self.auxiliary_heat_runtime[core_day_set.hourly].sum()
[docs] def total_emergency_heating_runtime(self, core_day_set):
""" Calculates total emergency heating runtime.
Parameters
----------
core_day_set : thermostat.core.CoreDaySet
Core day set for which to calculate total runtime.
Returns
-------
total_runtime : float
Total heating runtime.
"""
self._protect_aux_emerg()
return self.emergency_heat_runtime[core_day_set.hourly].sum()
[docs] def total_cooling_runtime(self, core_day_set):
""" Calculates total cooling runtime.
Parameters
----------
core_day_set : thermostat.core.CoreDaySet
Core day set for which to calculate total runtime.
Returns
-------
total_runtime : float
Total cooling runtime.
"""
self._protect_cooling()
return self.cool_runtime[core_day_set.daily].sum()
[docs] def get_resistance_heat_utilization_bins(self, core_heating_day_set):
""" Calculates resistance heat utilization metrics in temperature
bins of 5 degrees between 0 and 60 degrees Fahrenheit.
Parameters
----------
core_heating_day_set : thermostat.core.CoreDaySet
Core heating day set for which to calculate total runtime.
Returns
-------
RHUs : numpy.array or None
Resistance heat utilization for each temperature bin, ordered
ascending by temperature bin. Returns None if the thermostat does
not control the appropriate equipment
"""
self._protect_aux_emerg()
bin_step = 5
if self.equipment_type == 1:
RHUs = []
in_core_day_set_daily = self._get_range_boolean(
core_heating_day_set.daily.index,
core_heating_day_set.start_date,
core_heating_day_set.end_date)
# convert hourly to daily
temp_out_daily = self.temperature_out.resample('D').mean()
aux_daily = self.auxiliary_heat_runtime.resample('D').sum()
emg_daily = self.emergency_heat_runtime.resample('D').sum()
temperature_bins = [(i, i+5) for i in range(0, 60, 5)]
for low_temp, high_temp in temperature_bins:
temp_low_enough_daily = temp_out_daily < high_temp
temp_high_enough_daily = temp_out_daily >= low_temp
temp_bin_daily = temp_low_enough_daily & temp_high_enough_daily & in_core_day_set_daily
R_heat = self.heat_runtime[temp_bin_daily].sum()
R_aux = aux_daily[temp_bin_daily].sum()
R_emg = emg_daily[temp_bin_daily].sum()
try:
rhu = float(R_aux + R_emg) / float(R_heat + R_emg)
except ZeroDivisionError:
rhu = 0.
RHUs.append(rhu)
return np.array(RHUs)
else:
return None
[docs] def get_ignored_days(self, core_day_set):
""" Determine how many days are ignored for a particular core day set
Returns
-------
n_both : int
Number of days excluded from core day set because of presence of
both heating and cooling runtime.
n_days_insufficient : int
Number of days excluded from core day set because of null runtime
data.
"""
in_range = self._get_range_boolean(
core_day_set.daily.index,
core_day_set.start_date,
core_day_set.end_date)
if self.equipment_type in self.HEATING_EQUIPMENT_TYPES:
has_heating = self.heat_runtime > 0
null_heating = pd.isnull(self.heat_runtime)
else:
has_heating = False
null_heating = False # shouldn't be counted, so False, not True
if self.equipment_type in self.COOLING_EQUIPMENT_TYPES:
has_cooling = self.cool_runtime > 0
null_cooling = pd.isnull(self.cool_runtime)
else:
has_cooling = False
null_cooling = False # shouldn't be counted, so False, not True
n_both = (in_range & has_heating & has_cooling).sum()
n_days_insufficient = (in_range & (null_heating | null_cooling)).sum()
return n_both, n_days_insufficient
[docs] def get_core_day_set_n_days(self, core_day_set):
""" Returns number of days in the core day set.
"""
return int(core_day_set.daily.sum())
[docs] def get_cooling_demand(self, core_cooling_day_set):
"""
Calculates a measure of cooling demand using the hourlyavgCTD method.
Starting with an assumed value of zero for Tau :math:`(\\tau_c)`,
calculate the daily Cooling Thermal Demand :math:`(\\text{daily CTD}_d)`, as follows
:math:`\\text{daily CTD}_d = \\frac{\sum_{i=1}^{24} [\\tau_c - \\text{hourly} \Delta T_{d.n}]_{+}}{24}`, where
:math:`\\text{hourly} \Delta T_{d.n} (^{\circ} F) = \\text{hourly indoor} T_{d.n} - \\text{hourly outdoor} T_{d.n}`, and
:math:`d` is the core cooling day; :math:`\left(001, 002, 003 ... x \\right)`,
:math:`n` is the hour; :math:`\left(01, 02, 03 ... 24 \\right)`,
:math:`\\tau_c` (cooling) is the :math:`\Delta T` associated with :math:`CTD=0` (zero cooling runtime), and
:math:`[]_{+}` indicates that the term is zero if its value would be negative.
For the set of all core cooling days in the CT interval data file, use
ratio estimation to calculate :math:`\\alpha_c`, the home's
responsiveness to cooling, which should be positive.
:math:`\\alpha_c \left(\\frac{\\text{minutes}}{^{\circ} F}\\right) = \\frac{RT_\\text{actual cool}}{\sum_{d=1}^{x} \\text{daily CTD}_d}`, where
:math:`RT_\\text{actual cool}` is the sum of cooling run times for all core cooling days in the CT interval data file.
For the set of all core cooling days in the CT interval data file,
optimize :math:`\\tau_c` that results in minimization of the sum of
squares of the difference between daily run times reported by the CT,
and calculated daily cooling run times.
Next recalculate :math:`\\alpha_c` (in accordance with the above step)
and record the model's parameters :math:`\left(\\alpha_c, \\tau_c \\right)`
Parameters
----------
core_cooling_day_set : thermostat.core.CoreDaySet
Core day set over which to calculate cooling demand.
Returns
-------
demand : pd.Series
Daily demand in the core heating day set as calculated using the
method described above.
tau : float
Estimate of :math:`\\tau_c`.
alpha : float
Estimate of :math:`\\alpha_c`
mse : float
Mean squared error in runtime estimates.
rmse : float
Root mean squared error in runtime estimates.
cvrmse : float
Coefficient of variation of root mean squared error in runtime estimates.
mape : float
Mean absolute percent error
mae : float
Mean absolute error
"""
self._protect_cooling()
core_day_set_temp_in = self.temperature_in[core_cooling_day_set.hourly]
core_day_set_temp_out = self.temperature_out[core_cooling_day_set.hourly]
core_day_set_deltaT = core_day_set_temp_in - core_day_set_temp_out
daily_index = core_cooling_day_set.daily[core_cooling_day_set.daily].index
def calc_cdd(tau):
hourly_cdd = (tau - core_day_set_deltaT).apply(lambda x: np.maximum(x, 0))
# Note - `x / 24` this should be thought of as a unit conversion, not an average.
return np.array([cdd.sum() / 24 for day, cdd in hourly_cdd.groupby(core_day_set_deltaT.index.date)])
daily_runtime = self.cool_runtime[core_cooling_day_set.daily]
total_runtime = daily_runtime.sum()
def calc_estimates(tau):
cdd = calc_cdd(tau)
total_cdd = np.sum(cdd)
alpha_estimate = total_runtime / total_cdd
runtime_estimate = cdd * alpha_estimate
errors = daily_runtime - runtime_estimate
return cdd, alpha_estimate, errors
def estimate_errors(tau_estimate):
_, _, errors = calc_estimates(tau_estimate)
return errors
tau_starting_guess = 0
try:
y, _ = leastsq(estimate_errors, tau_starting_guess)
except TypeError: # len 0
assert daily_runtime.shape[0] == 0 # make sure no other type errors are sneaking in
return pd.Series([], index=daily_index), np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan
tau_estimate = y[0]
cdd, alpha_estimate, errors = calc_estimates(tau_estimate)
mse = np.nanmean((errors)**2)
rmse = mse ** 0.5
mean_daily_runtime = np.nanmean(daily_runtime)
cvrmse = rmse / mean_daily_runtime
mape = np.nanmean(np.absolute(errors / mean_daily_runtime))
mae = np.nanmean(np.absolute(errors))
return pd.Series(cdd, index=daily_index), tau_estimate, alpha_estimate, mse, rmse, cvrmse, mape, mae
[docs] def get_heating_demand(self, core_heating_day_set):
"""
Calculates a measure of heating demand using the hourlyavgCTD method.
:math:`\\text{daily HTD}_d = \\frac{\sum_{i=1}^{24} [\\text{hourly} \Delta T_{d.n} - \\tau_h]_{+}}{24}`, where
:math:`\\text{hourly} \Delta T_{d.n} (^{\circ} F) = \\text{hourly indoor} T_{d.n} - \\text{hourly outdoor} T_{d.n}`, and
:math:`d` is the core heating day; :math:`\left(001, 002, 003 ... x \\right)`,
:math:`n` is the hour; :math:`\left(01, 02, 03 ... 24 \\right)`,
:math:`\\tau_h` (heating) is the :math:`\Delta T` associated with :math:`HTD=0`, reflecting that homes with no heat running tend to be warmer that the outdoors, and
:math:`[]_{+}` indicates that the term is zero if its value would be negative.
For the set of all core heating days in the CT interval data file, use
ratio estimation to calculate :math:`\\alpha_h`, the home's
responsiveness to heating, which should be positive.
:math:`\\alpha_h \left(\\frac{\\text{minutes}}{^{\circ} F}\\right) = \\frac{RT_\\text{actual heat}}{\sum_{d=1}^{x} \\text{daily HTD}_d}`, where
:math:`RT_\\text{actual heat}` is the sum of heating run times for all core heating days in the CT interval data file.
For the set of all core heating days in the CT interval data file,
optimize :math:`\\tau_h` that results in minimization of the sum of
squares of the difference between daily run times reported by the CT,
and calculated daily heating run times.
Next recalculate :math:`\\alpha_h` (in accordance with the above step)
and record the model's parameters :math:`\left(\\alpha_h, \\tau_h \\right)`
Parameters
----------
core_heating_day_set : array_like
Core day set over which to calculate heating demand.
Returns
-------
demand : pd.Series
Daily demand in the core heating day set as calculated using the
method described above.
tau : float
Estimate of :math:`\\tau_h`.
alpha : float
Estimate of :math:`\\alpha_h`
mse : float
Mean squared error in runtime estimates.
rmse : float
Root mean squared error in runtime estimates.
cvrmse : float
Coefficient of variation of root mean squared error in runtime estimates.
mape : float
Mean absolute percent error
mae : float
Mean absolute error
"""
self._protect_heating()
core_day_set_temp_in = self.temperature_in[core_heating_day_set.hourly]
core_day_set_temp_out = self.temperature_out[core_heating_day_set.hourly]
core_day_set_deltaT = core_day_set_temp_in - core_day_set_temp_out
daily_index = core_heating_day_set.daily[core_heating_day_set.daily].index
def calc_hdd(tau):
hourly_hdd = (core_day_set_deltaT - tau).apply(lambda x: np.maximum(x, 0))
# Note - this `x / 24` should be thought of as a unit conversion, not an average.
return np.array([hdd.sum() / 24 for day, hdd in hourly_hdd.groupby(core_day_set_deltaT.index.date)])
daily_runtime = self.heat_runtime[core_heating_day_set.daily]
total_runtime = daily_runtime.sum()
def calc_estimates(tau):
hdd = calc_hdd(tau)
total_hdd = np.sum(hdd)
alpha_estimate = total_runtime / total_hdd
runtime_estimate = hdd * alpha_estimate
errors = daily_runtime - runtime_estimate
return hdd, alpha_estimate, errors
def estimate_errors(tau_estimate):
_, _, errors = calc_estimates(tau_estimate)
return errors
tau_starting_guess = 0
try:
y, _ = leastsq(estimate_errors, tau_starting_guess)
except TypeError: # len 0
assert daily_runtime.shape[0] == 0 # make sure no other type errors are sneaking in
return pd.Series([], index=daily_index), np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan
tau_estimate = y[0]
hdd, alpha_estimate, errors = calc_estimates(tau_estimate)
mse = np.nanmean((errors)**2)
rmse = mse ** 0.5
mean_daily_runtime = np.nanmean(daily_runtime)
cvrmse = rmse / mean_daily_runtime
mape = np.nanmean(np.absolute(errors / mean_daily_runtime))
mae = np.nanmean(np.absolute(errors))
return (
pd.Series(hdd, index=daily_index),
tau_estimate,
alpha_estimate,
mse,
rmse,
cvrmse,
mape,
mae
)
[docs] def get_core_cooling_day_baseline_setpoint(self, core_cooling_day_set,
method='tenth_percentile', source='temperature_in'):
""" Calculate the core cooling day baseline setpoint (comfort
temperature).
Parameters
----------
core_cooling_day_set : thermost.core.CoreDaySet
Core cooling days over which to calculate baseline cooling setpoint.
method : {"tenth_percentile"}, default: "tenth_percentile"
Method to use in calculation of the baseline.
- "tenth_percentile": 10th percentile of source temperature.
(Either cooling setpoint or temperature in).
source : {"cooling_setpoint", "temperature_in"}, default "temperature_in"
The source of temperatures to use in baseline calculation.
Returns
-------
baseline : float
The baseline cooling setpoint for the core cooling days as determined
by the given method.
"""
self._protect_cooling()
if method == 'tenth_percentile':
if source == 'cooling_setpoint':
return self.cooling_setpoint[core_cooling_day_set.hourly].dropna().quantile(.1)
elif source == 'temperature_in':
return self.temperature_in[core_cooling_day_set.hourly].dropna().quantile(.1)
else:
raise NotImplementedError
else:
raise NotImplementedError
[docs] def get_core_heating_day_baseline_setpoint(self, core_heating_day_set,
method='ninetieth_percentile', source='temperature_in'):
""" Calculate the core heating day baseline setpoint (comfort temperature).
Parameters
----------
core_heating_day_set : thermostat.core.CoreDaySet
Core heating days over which to calculate baseline heating setpoint.
method : {"ninetieth_percentile"}, default: "ninetieth_percentile"
Method to use in calculation of the baseline.
- "ninetieth_percentile": 90th percentile of source temperature.
(Either heating setpoint or indoor temperature).
source : {"heating_setpoint", "temperature_in"}, default "temperature_in"
The source of temperatures to use in baseline calculation.
Returns
-------
baseline : float
The baseline heating setpoint for the heating day as determined
by the given method.
"""
self._protect_heating()
if method == 'ninetieth_percentile':
if source == 'heating_setpoint':
return self.heating_setpoint[core_heating_day_set.hourly].dropna().quantile(.9)
elif source == 'temperature_in':
return self.temperature_in[core_heating_day_set.hourly].dropna().quantile(.9)
else:
raise NotImplementedError
else:
raise NotImplementedError
[docs] def get_baseline_cooling_demand(self, core_cooling_day_set, temp_baseline, tau):
""" Calculate baseline cooling demand for a particular core cooling
day set and fitted physical parameters.
:math:`\\text{daily CTD base}_d = \\frac{\sum_{i=1}^{24} [\\tau_c - \\text{hourly } \Delta T \\text{ base cool}_{d.n}]_{+}}{24}`, where
:math:`\\text{hourly } \Delta T \\text{ base cool}_{d.n} (^{\circ} F) = \\text{base heat} T_{d.n} - \\text{hourly outdoor} T_{d.n}`, and
:math:`d` is the core cooling day; :math:`\left(001, 002, 003 ... x \\right)`,
:math:`n` is the hour; :math:`\left(01, 02, 03 ... 24 \\right)`,
:math:`\\tau_c` (cooling), determined earlier, is a constant that is part of the CT/home's thermal/HVAC cooling run time model, and
:math:`[]_{+}` indicates that the term is zero if its value would be negative.
Parameters
----------
core_cooling_day_set : thermostat.core.CoreDaySet
Core cooling days over which to calculate baseline cooling demand.
temp_baseline : float
Baseline comfort temperature
tau : float, default: None
From fitted demand model.
Returns
-------
baseline_cooling_demand : pandas.Series
A series containing baseline daily heating demand for the core
cooling day set.
"""
self._protect_cooling()
hourly_temp_out = self.temperature_out[core_cooling_day_set.hourly]
hourly_cdd = (tau - (temp_baseline - hourly_temp_out)).apply(lambda x: np.maximum(x, 0))
demand = np.array([cdd.sum() / 24 for day, cdd in hourly_cdd.groupby(hourly_temp_out.index.date)])
index = core_cooling_day_set.daily[core_cooling_day_set.daily].index
return pd.Series(demand, index=index)
[docs] def get_baseline_heating_demand(self, core_heating_day_set, temp_baseline, tau):
""" Calculate baseline heating demand for a particular core heating day
set and fitted physical parameters.
:math:`\\text{daily HTD base}_d = \\frac{\sum_{i=1}^{24} [\\text{hourly } \Delta T \\text{ base heat}_{d.n} - \\tau_h]_{+}}{24}`, where
:math:`\\text{hourly } \Delta T \\text{ base heat}_{d.n} (^{\circ} F) = \\text{base cool} T_{d.n} - \\text{hourly outdoor} T_{d.n}`, and
:math:`d` is the core heating day; :math:`\left(001, 002, 003 ... x \\right)`,
:math:`n` is the hour; :math:`\left(01, 02, 03 ... 24 \\right)`,
:math:`\\tau_h` (heating), determined earlier, is a constant that is part of the CT/home's thermal/HVAC heating run time model, and
:math:`[]_{+}` indicates that the term is zero if its value would be negative.
Parameters
----------
core_heating_day_set : thermostat.core.CoreDaySet
Core heating days over which to calculate baseline cooling demand.
temp_baseline : float
Baseline comfort temperature
tau : float, default: None
From fitted demand model.
Returns
-------
baseline_heating_demand : pandas.Series
A series containing baseline daily heating demand for the core heating days.
"""
self._protect_heating()
hourly_temp_out = self.temperature_out[core_heating_day_set.hourly]
hourly_hdd = (temp_baseline - hourly_temp_out - tau).apply(lambda x: np.maximum(x, 0))
demand = np.array([hdd.sum() / 24 for day, hdd in hourly_hdd.groupby(hourly_temp_out.index.date)])
index = core_heating_day_set.daily[core_heating_day_set.daily].index
return pd.Series(demand, index=index)
[docs] def get_baseline_cooling_runtime(self, baseline_cooling_demand, alpha):
""" Calculate baseline cooling runtime given baseline cooling demand
and fitted physical parameters.
:math:`RT_{\\text{base cool}} (\\text{minutes}) = \\alpha_c \cdot \\text{daily CTD base}_d`
Parameters
----------
baseline_cooling_demand : pandas.Series
A series containing estimated daily baseline cooling demand.
alpha : float
Slope of fitted line
Returns
-------
baseline_cooling_runtime : pandas.Series
A series containing estimated daily baseline cooling runtime.
"""
return np.maximum(alpha * (baseline_cooling_demand), 0)
[docs] def get_baseline_heating_runtime(self, baseline_heating_demand, alpha):
""" Calculate baseline heating runtime given baseline heating demand.
and fitted physical parameters.
:math:`RT_{\\text{base heat}} (\\text{minutes}) = \\alpha_h \cdot \\text{daily HTD base}_d`
Parameters
----------
baseline_heating_demand : pandas.Series
A series containing estimated daily baseline heating demand.
alpha : float
Slope of fitted line
Returns
-------
baseline_heating_runtime : pandas.Series
A series containing estimated daily baseline heating runtime.
"""
return np.maximum(alpha * (baseline_heating_demand), 0)
def get_daily_avoided_cooling_runtime(
self, baseline_runtime, core_cooling_day_set):
return baseline_runtime - self.cool_runtime[core_cooling_day_set]
def get_daily_avoided_heating_runtime(
self, baseline_runtime, core_heating_day_set):
return baseline_runtime - self.heat_runtime[core_heating_day_set]
[docs] def calculate_epa_field_savings_metrics(self,
core_cooling_day_set_method="entire_dataset",
core_heating_day_set_method="entire_dataset",
climate_zone_mapping=None):
""" Calculates metrics for connected thermostat savings as defined by
the specification defined by the EPA Energy Star program and stakeholders.
Parameters
----------
core_cooling_day_set_method : {"entire_dataset", "year_end_to_end"}, default: "entire_dataset"
Method by which to find core cooling day sets.
- "entire_dataset": all core cooling days in dataset (days with >= 1
hour of cooling runtime and no heating runtime.
- "year_end_to_end": groups all core cooling days (days with >= 1 hour of total
cooling and no heating) from January 1 to December 31 into
independent core cooling day sets.
core_heating_day_set_method : {"entire_dataset", "year_mid_to_mid"}, default: "entire_dataset"
Method by which to find core heating day sets.
- "entire_dataset": all core heating days in dataset (days with >= 1
hour of heating runtime and no cooling runtime.
- "year_mid_to_mid": groups all core heating days (days with >= 1 hour
of total heating and no cooling) from July 1 to June 30 into
independent core heating day sets.
climate_zone_mapping : filename, default: None
A mapping from climate zone to zipcode. If None is provided, uses
default zipcode to climate zone mapping provided in tutorial.
:download:`default mapping <./resources/Building America Climate Zone to Zipcode Database_Rev2_2016.09.08.csv>`
Returns
-------
metrics : list
list of dictionaries of output metrics; one per set of core heating
or cooling days.
"""
def _load_mapping(filename_or_buffer):
df = pd.read_csv(
filename_or_buffer,
usecols=["zipcode", "group"],
dtype={"zipcode": str, "group": str},
).set_index('zipcode').drop('zipcode')
df = df.where((pd.notnull(df)), None)
return dict(df.to_records('index'))
if climate_zone_mapping is None:
with resource_stream('thermostat.resources',
'Building America Climate Zone to Zipcode Database_Rev2_2016.09.08.csv') as f:
mapping = _load_mapping(f)
else:
try:
mapping = _load_mapping(climate_zone_mapping)
except:
raise ValueError("Could not load climate zone mapping")
with resource_stream('thermostat.resources', 'regional_baselines.csv') as f:
df = pd.read_csv(
f, usecols=[
'EIA Climate Zone',
'Baseline heating temp (F)',
'Baseline cooling temp (F)'
])
df = df.where((pd.notnull(df)), None)
df = df.set_index('EIA Climate Zone')
cooling_regional_baseline_temps = { k: v for k, v in df['Baseline cooling temp (F)'].iteritems()}
heating_regional_baseline_temps = { k: v for k, v in df['Baseline heating temp (F)'].iteritems()}
climate_zone = mapping.get(self.zipcode)
baseline_regional_cooling_comfort_temperature = cooling_regional_baseline_temps.get(climate_zone, None)
baseline_regional_heating_comfort_temperature = heating_regional_baseline_temps.get(climate_zone, None)
metrics = []
def avoided(baseline, observed):
return baseline - observed
def percent_savings(avoided, baseline):
return (avoided.mean() / baseline.mean()) * 100.0
if self.equipment_type in self.COOLING_EQUIPMENT_TYPES:
for core_cooling_day_set in self.get_core_cooling_days(
method=core_cooling_day_set_method):
baseline10_comfort_temperature = \
self.get_core_cooling_day_baseline_setpoint(core_cooling_day_set)
daily_runtime = self.cool_runtime[core_cooling_day_set.daily]
(
demand,
tau,
alpha,
mse,
rmse,
cvrmse,
mape,
mae,
) = self.get_cooling_demand(core_cooling_day_set)
total_runtime_core_cooling = daily_runtime.sum()
n_days = core_cooling_day_set.daily.sum()
average_daily_cooling_runtime = \
total_runtime_core_cooling / n_days
baseline10_demand = self.get_baseline_cooling_demand(
core_cooling_day_set,
baseline10_comfort_temperature,
tau,
)
baseline10_runtime = self.get_baseline_cooling_runtime(
baseline10_demand,
alpha
)
avoided_runtime_baseline10 = avoided(baseline10_runtime, daily_runtime)
savings_baseline10 = percent_savings(avoided_runtime_baseline10, baseline10_runtime)
if baseline_regional_cooling_comfort_temperature is not None:
baseline_regional_demand = self.get_baseline_cooling_demand(
core_cooling_day_set,
baseline_regional_cooling_comfort_temperature,
tau
)
baseline_regional_runtime = self.get_baseline_cooling_runtime(
baseline_regional_demand,
alpha
)
avoided_runtime_baseline_regional = avoided(baseline_regional_runtime, daily_runtime)
savings_baseline_regional = percent_savings(avoided_runtime_baseline_regional, baseline_regional_runtime)
percent_savings_baseline_regional = savings_baseline_regional
avoided_daily_mean_core_day_runtime_baseline_regional = avoided_runtime_baseline_regional.mean()
avoided_total_core_day_runtime_baseline_regional = avoided_runtime_baseline_regional.sum()
baseline_daily_mean_core_day_runtime_baseline_regional = baseline_regional_runtime.mean()
baseline_total_core_day_runtime_baseline_regional = baseline_regional_runtime.sum()
_daily_mean_core_day_demand_baseline_baseline_regional = np.nanmean(baseline_regional_demand)
else:
baseline_regional_demand = None
baseline_regional_runtime = None
avoided_runtime_baseline_regional = None
savings_baseline_regional = None
percent_savings_baseline_regional = None
avoided_daily_mean_core_day_runtime_baseline_regional = None
avoided_total_core_day_runtime_baseline_regional = None
baseline_daily_mean_core_day_runtime_baseline_regional = None
baseline_total_core_day_runtime_baseline_regional = None
_daily_mean_core_day_demand_baseline_baseline_regional = None
n_days_both, n_days_insufficient_data = self.get_ignored_days(core_cooling_day_set)
n_core_cooling_days = self.get_core_day_set_n_days(core_cooling_day_set)
n_days_in_inputfile_date_range = self.get_inputfile_date_range(core_cooling_day_set)
outputs = {
"sw_version": get_version(),
"ct_identifier": self.thermostat_id,
"equipment_type": self.equipment_type,
"heating_or_cooling": core_cooling_day_set.name,
"zipcode": self.zipcode,
"station": self.station,
"climate_zone": climate_zone,
"start_date": pd.Timestamp(core_cooling_day_set.start_date).to_datetime().isoformat(),
"end_date": pd.Timestamp(core_cooling_day_set.end_date).to_datetime().isoformat(),
"n_days_in_inputfile_date_range": n_days_in_inputfile_date_range,
"n_days_both_heating_and_cooling": n_days_both,
"n_days_insufficient_data": n_days_insufficient_data,
"n_core_cooling_days": n_core_cooling_days,
"baseline_percentile_core_cooling_comfort_temperature": baseline10_comfort_temperature,
"regional_average_baseline_cooling_comfort_temperature": baseline_regional_cooling_comfort_temperature,
"percent_savings_baseline_percentile": savings_baseline10,
"avoided_daily_mean_core_day_runtime_baseline_percentile": avoided_runtime_baseline10.mean(),
"avoided_total_core_day_runtime_baseline_percentile": avoided_runtime_baseline10.sum(),
"baseline_daily_mean_core_day_runtime_baseline_percentile": baseline10_runtime.mean(),
"baseline_total_core_day_runtime_baseline_percentile": baseline10_runtime.sum(),
"_daily_mean_core_day_demand_baseline_baseline_percentile": np.nanmean(baseline10_demand),
"percent_savings_baseline_regional": percent_savings_baseline_regional,
"avoided_daily_mean_core_day_runtime_baseline_regional": avoided_daily_mean_core_day_runtime_baseline_regional,
"avoided_total_core_day_runtime_baseline_regional": avoided_total_core_day_runtime_baseline_regional,
"baseline_daily_mean_core_day_runtime_baseline_regional": baseline_daily_mean_core_day_runtime_baseline_regional,
"baseline_total_core_day_runtime_baseline_regional": baseline_total_core_day_runtime_baseline_regional,
"_daily_mean_core_day_demand_baseline_baseline_regional": _daily_mean_core_day_demand_baseline_baseline_regional,
"mean_demand": np.nanmean(demand),
"tau": tau,
"alpha": alpha,
"mean_sq_err": mse,
"root_mean_sq_err": rmse,
"cv_root_mean_sq_err": cvrmse,
"mean_abs_pct_err": mape,
"mean_abs_err": mae,
"total_core_cooling_runtime": total_runtime_core_cooling,
"daily_mean_core_cooling_runtime": average_daily_cooling_runtime,
}
metrics.append(outputs)
if self.equipment_type in self.HEATING_EQUIPMENT_TYPES:
for core_heating_day_set in self.get_core_heating_days(method=core_heating_day_set_method):
baseline90_comfort_temperature = \
self.get_core_heating_day_baseline_setpoint(core_heating_day_set)
# deltaT
daily_runtime = self.heat_runtime[core_heating_day_set.daily]
(
demand,
tau,
alpha,
mse,
rmse,
cvrmse,
mape,
mae,
) = self.get_heating_demand(core_heating_day_set)
total_runtime_core_heating = daily_runtime.sum()
n_days = core_heating_day_set.daily.sum()
average_daily_heating_runtime = \
total_runtime_core_heating / n_days
baseline90_demand = self.get_baseline_heating_demand(
core_heating_day_set,
baseline90_comfort_temperature,
tau,
)
baseline90_runtime = self.get_baseline_heating_runtime(
baseline90_demand,
alpha,
)
avoided_runtime_baseline90 = avoided(baseline90_runtime, daily_runtime)
savings_baseline90 = percent_savings(avoided_runtime_baseline90, baseline90_runtime)
if baseline_regional_heating_comfort_temperature is not None:
baseline_regional_demand = self.get_baseline_heating_demand(
core_heating_day_set,
baseline_regional_heating_comfort_temperature,
tau,
)
baseline_regional_runtime = self.get_baseline_heating_runtime(
baseline_regional_demand,
alpha,
)
avoided_runtime_baseline_regional = avoided(baseline_regional_runtime, daily_runtime)
savings_baseline_regional = percent_savings(avoided_runtime_baseline_regional, baseline_regional_runtime)
percent_savings_baseline_regional = savings_baseline_regional
avoided_daily_mean_core_day_runtime_baseline_regional = avoided_runtime_baseline_regional.mean()
avoided_total_core_day_runtime_baseline_regional = avoided_runtime_baseline_regional.sum()
baseline_daily_mean_core_day_runtime_baseline_regional = baseline_regional_runtime.mean()
baseline_total_core_day_runtime_baseline_regional = baseline_regional_runtime.sum()
_daily_mean_core_day_demand_baseline_baseline_regional = np.nanmean(baseline_regional_demand)
else:
baseline_regional_demand = None
baseline_regional_runtime = None
avoided_runtime_baseline_regional = None
savings_baseline_regional = None
percent_savings_baseline_regional = None
avoided_daily_mean_core_day_runtime_baseline_regional = None
avoided_total_core_day_runtime_baseline_regional = None
baseline_daily_mean_core_day_runtime_baseline_regional = None
baseline_total_core_day_runtime_baseline_regional = None
_daily_mean_core_day_demand_baseline_baseline_regional = None
n_days_both, n_days_insufficient_data = self.get_ignored_days(core_heating_day_set)
n_core_heating_days = self.get_core_day_set_n_days(core_heating_day_set)
n_days_in_inputfile_date_range = self.get_inputfile_date_range(core_heating_day_set)
outputs = {
"sw_version": get_version(),
"ct_identifier": self.thermostat_id,
"equipment_type": self.equipment_type,
"heating_or_cooling": core_heating_day_set.name,
"zipcode": self.zipcode,
"station": self.station,
"climate_zone": mapping.get(self.zipcode),
"start_date": pd.Timestamp(core_heating_day_set.start_date).to_datetime().isoformat(),
"end_date": pd.Timestamp(core_heating_day_set.end_date).to_datetime().isoformat(),
"n_days_in_inputfile_date_range": n_days_in_inputfile_date_range,
"n_days_both_heating_and_cooling": n_days_both,
"n_days_insufficient_data": n_days_insufficient_data,
"n_core_heating_days": n_core_heating_days,
"baseline_percentile_core_heating_comfort_temperature": baseline90_comfort_temperature,
"regional_average_baseline_heating_comfort_temperature": baseline_regional_heating_comfort_temperature,
"percent_savings_baseline_percentile": savings_baseline90,
"avoided_daily_mean_core_day_runtime_baseline_percentile": avoided_runtime_baseline90.mean(),
"avoided_total_core_day_runtime_baseline_percentile": avoided_runtime_baseline90.sum(),
"baseline_daily_mean_core_day_runtime_baseline_percentile": baseline90_runtime.mean(),
"baseline_total_core_day_runtime_baseline_percentile": baseline90_runtime.sum(),
"_daily_mean_core_day_demand_baseline_baseline_percentile": np.nanmean(baseline90_demand),
"percent_savings_baseline_regional": savings_baseline_regional,
"avoided_daily_mean_core_day_runtime_baseline_regional": avoided_daily_mean_core_day_runtime_baseline_regional,
"avoided_total_core_day_runtime_baseline_regional": avoided_total_core_day_runtime_baseline_regional,
"baseline_daily_mean_core_day_runtime_baseline_regional": baseline_daily_mean_core_day_runtime_baseline_regional,
"baseline_total_core_day_runtime_baseline_regional": baseline_total_core_day_runtime_baseline_regional,
"_daily_mean_core_day_demand_baseline_baseline_regional": _daily_mean_core_day_demand_baseline_baseline_regional,
"mean_demand": np.nanmean(demand),
"tau": tau,
"alpha": alpha,
"mean_sq_err": mse,
"root_mean_sq_err": rmse,
"cv_root_mean_sq_err": cvrmse,
"mean_abs_pct_err": mape,
"mean_abs_err": mae,
"total_core_heating_runtime": total_runtime_core_heating,
"daily_mean_core_heating_runtime": average_daily_heating_runtime,
}
if self.equipment_type in self.AUX_EMERG_EQUIPMENT_TYPES:
additional_outputs = {
"total_auxiliary_heating_core_day_runtime":
self.total_auxiliary_heating_runtime(
core_heating_day_set),
"total_emergency_heating_core_day_runtime":
self.total_emergency_heating_runtime(
core_heating_day_set),
}
rhus = self.get_resistance_heat_utilization_bins(core_heating_day_set)
if rhus is None:
for low, high in [(i, i+5) for i in range(0, 60, 5)]:
column = "rhu_{:02d}F_to_{:02d}F".format(low, high)
additional_outputs[column] = None
else:
for rhu, (low, high) in zip(rhus, [(i, i+5) for i in range(0, 60, 5)]):
column = "rhu_{:02d}F_to_{:02d}F".format(low, high)
additional_outputs[column] = rhu
outputs.update(additional_outputs)
metrics.append(outputs)
return metrics