Neural Network Load Forecasting with Weather Ensemble Predictions

In recent years, a large literature has evolved on the use of artificial neural networks (NNs) for electric load forecasting. NNs are particularly appealing because of their ability to model an unspecified non-linear relationship between load and weather variables. Weather forecasts are a key input when the NN is used for forecasting. This study Investigates the use of weather ensemble predictions in the application of NNs to load forecasting for lead times from 1 to 10 days ahead. A weather ensemble prediction consists of multiple scenarios for a weather variable. We use these scenarios to produce multiple scenarios for load. The results show that the average of the load scenarios is a more accurate load forecast than that produced using traditional weather forecasts. We use the load scenarios to estimate the uncertainty in the NN load forecast This compares favourably with estimates based solely on historical load forecast errors.