def process_request(self, arg_set):
inst_args = ["load_data", "temp_data", "timezone", "temp_units", "sq_ft"]
inst_args = { a_name: arg_set[a_name] for a_name in inst_args if arg_set.get(a_name)}
event_args = ["start_at", "end_at"]
event_args = { a_name: arg_set[a_name] for a_name in event_args if arg_set.get(a_name)}
ls = Loadshape(**inst_args)
event_performance_data = ls.event_performance(**event_args)
return event_performance_data
def process_request(self, arg_set):
inst_args = [
"load_data", "temp_data", "timezone", "temp_units", "sq_ft"
]
inst_args = {
a_name: arg_set[a_name]
for a_name in inst_args if arg_set.get(a_name)
}
event_args = ["start_at", "end_at"]
event_args = {
a_name: arg_set[a_name]
for a_name in event_args if arg_set.get(a_name)
}
ls = Loadshape(**inst_args)
event_performance_data = ls.event_performance(**event_args)
return event_performance_data
my_load_shape.add_named_exclusion("US_HOLIDAYS")
# ----- add tariff to enable cost calculations ----- #
tariff = Tariff(tariff_file=TARIFF, timezone='America/Los_Angeles')
tariff.add_dr_period("2013-09-23 14:00:00", "2013-09-23 16:00:00")
tariff.add_dr_period("2013-09-27 14:00:00", "2013-09-27 16:15:00")
my_load_shape.set_tariff(tariff)
# ----- build the baseline to use as a reference for performance ----- #
event_baseline = my_load_shape.baseline(weighting_days=14,
modeling_interval=900,
step_size=900)
# ----- calculate the performance summary for the event period ----- #
event_performance = my_load_shape.event_performance(DR_EVENT_START, DR_EVENT_END)
# ----- calculate the performance summary for the whole day ----- #
event_day_performance = my_load_shape.event_performance(DR_EVENT_DAY_START, DR_EVENT_DAY_END)
out = { "power_data": {} }
out["name"] = "DR Event - %s" % DR_EVENT_DAY_START
out["building"] = BUILDING_NAME
out["event_start_at"] = DR_EVENT_START
out["event_end_at"] = DR_EVENT_END
out["dr_event_stats"] = event_performance
out["dr_event_day_stats"] = event_day_performance
out["power_data"]["actual"] = my_load_shape.actual_data(DR_EVENT_DAY_START, DR_EVENT_DAY_END, step_size=900)
out["power_data"]["baseline"] = my_load_shape.baseline_data(DR_EVENT_DAY_START, DR_EVENT_DAY_END, step_size=900)
# ----- write output to file ----- #
my_load_shape.add_exclusion("2013-09-23 00:00:00", "2013-09-24 00:00:00")
my_load_shape.add_exclusion("2013-09-27 00:00:00", "2013-09-28 00:00:00")
my_load_shape.add_named_exclusion("US_HOLIDAYS")
# ----- generate the appropriate baseline ----- #
baseline = my_load_shape.baseline(weighting_days=CUMULATIVE_SUM_WEIGHTING_DAYS,
modeling_interval=900,
step_size=900)
# ----- set up the cumulative sum dates ----- #
tz = utils.get_timezone('America/Los_Angeles')
start_at = utils.str_to_datetime(CUMULATIVE_SUM_START, tz)
end_at = utils.int_to_datetime(my_load_shape.baseline_series.end_at(), tz)
# ----- calculate long term event performance and cumulative sum series ----- #
event_performance = my_load_shape.event_performance(start_at, end_at)
cumulative_sum_series = my_load_shape.cumulative_sum(start_at, end_at)
# ----- assemble a payload summarizng the cumulative sum ----- #
out = {}
out["building"] = BUILDING_NAME
out["event_name"] = CUMULATIVE_SUM_NAME
out["sum_start_at"] = start_at.strftime("%Y-%m-%d %H:%M:%S")
out["sum_end_at"] = end_at.strftime("%Y-%m-%d %H:%M:%S")
out["event_performance"] = event_performance
out["cumulative_sum_data"] = cumulative_sum_series.data()
# ----- write output to file ----- #
file_name = path.join(EXAMPLES_DIR, "output", "cumulative-sum-example.json")
write_json(data=out, file_name=file_name)
my_load_shape.add_exclusion("2013-09-23 00:00:00", "2013-09-24 00:00:00")
my_load_shape.add_exclusion("2013-09-27 00:00:00", "2013-09-28 00:00:00")
my_load_shape.add_named_exclusion("US_HOLIDAYS")
# ----- generate the appropriate baseline ----- #
baseline = my_load_shape.baseline(weighting_days=CUMULATIVE_SUM_WEIGHTING_DAYS,
modeling_interval=900,
step_size=900)
# ----- set up the cumulative sum dates ----- #
tz = utils.get_timezone('America/Los_Angeles')
start_at = utils.str_to_datetime(CUMULATIVE_SUM_START, tz)
end_at = utils.int_to_datetime(my_load_shape.baseline_series.end_at(), tz)
# ----- calculate long term event performance and cumulative sum series ----- #
event_performance = my_load_shape.event_performance(start_at, end_at)
cumulative_sum_series = my_load_shape.cumulative_sum(start_at, end_at)
# ----- assemble a payload summarizng the cumulative sum ----- #
out = {}
out["building"] = BUILDING_NAME
out["event_name"] = CUMULATIVE_SUM_NAME
out["sum_start_at"] = start_at.strftime("%Y-%m-%d %H:%M:%S")
out["sum_end_at"] = end_at.strftime("%Y-%m-%d %H:%M:%S")
out["event_performance"] = event_performance
out["cumulative_sum_data"] = cumulative_sum_series.data()
# ----- write output to file ----- #
file_name = path.join(EXAMPLES_DIR, "output", "cumulative-sum-example.json")
write_json(data=out, file_name=file_name)
