Predictor Comparison
This script analyzes the predictive power of different sets of predictors on day-ahead electricity price forecasting
Contents
Import data and generate all predictors
Load pre-trained networks
Baseline performance with all predictors
Compute performance of best neural network with all predictors
Price, Load & Fuel
Do the hour, weekday, holiday and temperature variables provide any predictive power when the load is known?
Selected Predictors: CurrentLoad PrevWeekSameHourLoad prevDaySameHourLoad prev24HrAveLoad PrevWeekSameHourPrice prevDaySameHourPrice prev24HrAvePrice prevDayNGPrice prevWeekAveNGPrice Baseline error with 14 predictors: MAE = $5.21, MAPE = 6.57% Error with selected 9 predictors: MAE = $5.69, MAPE = 7.11%
No Load Information
How accurate is the prediction if the load information is not known?
Selected Predictors: DryBulb DewPoint Hour Weekday IsWorkingDay PrevWeekSameHourPrice prevDaySameHourPrice prev24HrAvePrice prevDayNGPrice prevWeekAveNGPrice Baseline error with 14 predictors: MAE = $5.21, MAPE = 6.57% Error with selected 10 predictors: MAE = $5.98, MAPE = 7.31%
No Current Load Information
How accurate is the prediction if the real-time load is not known?
Selected Predictors: DryBulb DewPoint Hour Weekday IsWorkingDay PrevWeekSameHourLoad prevDaySameHourLoad prev24HrAveLoad PrevWeekSameHourPrice prevDaySameHourPrice prev24HrAvePrice prevDayNGPrice prevWeekAveNGPrice Baseline error with 14 predictors: MAE = $5.21, MAPE = 6.57% Error with selected 13 predictors: MAE = $5.70, MAPE = 7.15%