def test_fri(self):
dt = datetime(2019, 8, 16, 0, 0)
self.assertTrue(timeseries.is_weekday(dt))
def test_sun(self):
dt = datetime(2019, 8, 18, 0, 0)
self.assertFalse(timeseries.is_weekday(dt))
def fit_for_load(load_data, weather_data, selection_data=None,
prediction_datetime=None):
"""Combine load and weather data, filter by time (day of week and
time of day), and then get a ZIP model by calling
pyvvo.zip.get_best_fit_from_clustering.
:param load_data: Pandas DataFrame. Return from get_data_for_load.
:param weather_data: Pandas DataFrame which has originated from
gridappsd_platform.PlatformManager.get_weather. The data is
already assumed to be cleaned, e.g. it has already been passed
through timeseries.fix_ghi.
:param selection_data: Pandas Series, used as the point in space to
which cluster distances are measured. Passed directly to
zip.get_best_fit_from_clustering. Note all values of the index
must be columns in either load_data or weather_data, and voltage
('v') is not allowed. If None, the last 'temperature' and 'ghi'
values after DataFrame merging and interpolation will be used.
:param prediction_datetime: Optional. datetime.datetime object,
representing the starting time of the interval for which the
load model will be used to make predictions. If not provided,
it will be inferred from the last entry in either load_data or
weather_data, whichever has the later time.
NOTE 1: It's assumed that load_data and weather_data were pulled
using the same starting and ending times.
NOTE 2: These two DataFrames will be merged, and any resulting
NaN values will be filled by simple linear interpolation. Thus,
it's the caller's responsibility to ensure reasonable alignment
between the indices of the DataFrames.
:returns: output from pyvvo.zip.get_best_fit_from_clustering.
TODO: Should this filtering and joining be moved? It seems like it
could be excessive to do this for every single load. Maybe it
would be better to create one big DataFrame with all the load
data?
Back of the napkin:
2000 loads * 15 minutes * 4 intervals/hour * 24 hour/day
* 7 days/week * 2 weeks * 3 parameters * 8 bytes/parameter
* 1 MB/1,000,000 bytes = 968 MB.
We may not want to suck that into memory. We could "chunk" it
somehow. Well, we'll cross that bridge later.
"""
# Join our load_data and weather_data, fill gaps via time-based
# linear interpolation.
df = load_data.join(weather_data, how='outer').interpolate(method='time')
# If the indices didn't line up, we'll backfill and forward fill
# the rest. If this is ever necessary, it should just be for the
# first and last rows.
df.fillna(method='backfill', inplace=True)
df.fillna(method='ffill', inplace=True)
# Get interval string from the configuration.
interval_str = CONFIG['load_model']['averaging_interval']
# At this point, our df may not have an evenly spaced index. So,
# we need to determine if we're upsampling or downsampling.
# noinspection PyUnresolvedReferences
f1 = pd.tseries.frequencies.to_offset(
pd.infer_freq(weather_data.index))
# noinspection PyUnresolvedReferences
f2 = pd.tseries.frequencies.to_offset(pd.infer_freq(load_data.index))
if (f1 is None) and (f2 is not None):
min_f = f2
elif (f1 is not None) and (f2 is None):
min_f = f1
elif (f1 is not None) and (f2 is not None):
min_f = min(f1, f2)
else:
raise ValueError('Neither the given load_data nor weather_data '
'have an evenly spaced index. This makes resampling '
'impossible, and is not acceptable.')
# Determine if we're upsampling or downsampling.
method = timeseries.up_or_down_sample(orig_interval=min_f,
new_interval=interval_str)
if method is not None:
df = timeseries.resample_timeseries(ts=df, method=method,
interval_str=interval_str)
# If not given selection_data, use the last weather values.
if selection_data is None:
selection_data = df.iloc[-1][['temperature', 'ghi']]
# If not given prediction_time, infer it from the end of the index.
if prediction_datetime is None:
prediction_datetime = df.index[-1]
if timeseries.is_weekday(prediction_datetime):
df_dow = timeseries.filter_by_weekday(df)
else:
df_dow = timeseries.filter_by_weekend(df)
# Filter data by time. Start by getting some time ranges.
t = prediction_datetime.time()
td = timedelta(minutes=CONFIG['load_model']['filtering_interval_minutes'])
t_start = utils.add_timedelta_to_time(t=t, td=-td)
t_end = utils.add_timedelta_to_time(t=t, td=td)
df_dow_t = timeseries.filter_by_time(t_start=t_start, t_end=t_end,
data=df_dow)
# Now that our data's ready, let's perform the fit.
output = zip.get_best_fit_from_clustering(
data=df_dow_t, zip_fit_inputs={'v_n': FIT_NOMINAL_VOLTAGE},
selection_data=selection_data
)
return output
