def predict_2017(cls):
from market import Market
import helpers
from dateutil import parser
forecaster = TemperatureForecastModel('/home/hngo/PycharmProjects/volttron-applications/pnnl/TNSAgent/campus_config')
# Create market with some time intervals
mkt = Market()
analysis_time = parser.parse("2017-01-01 00:00:00")
mkt.marketClearingTime = analysis_time
mkt.nextMarketClearingTime = mkt.marketClearingTime + mkt.marketClearingInterval
# Control steps using horizon
mkt.futureHorizon = timedelta(days=365)
mkt.check_intervals(analysis_time)
# set time intervals
forecaster.update_information(mkt)
# schedule powers
predictor = OpenLoopPnnlLoadPredictor(forecaster)
predictor.schedule_power(mkt)
powers = [(x.timeInterval.startTime, x.value) for x in predictor.scheduledPowers]
total_power = sum([s[1] for s in powers])
print(powers)
print(total_power)
MeasurementType.PredictedValue,
temp)
self.predictedValues.append(interval_value)
if __name__ == '__main__':
from market import Market
import helpers
forecaster = TemperatureForecastModel(
'/home/hngo/PycharmProjects/volttron-applications/pnnl/TNSAgent/campus_config.json'
)
# Create market with some time intervals
mkt = Market()
mkt.marketClearingTime = datetime.now().replace(minute=0,
second=0,
microsecond=0)
mkt.nextMarketClearingTime = mkt.marketClearingTime + mkt.marketClearingInterval
mkt.check_intervals()
# Test update_information
forecaster.update_information(mkt)
times = [
helpers.format_ts(x.timeInterval.startTime)
for x in forecaster.predictedValues
]
print(times)
