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)