Abstract
Electricity demand forecast plays a major role in the planning and resource allocation phase of utility companies. In particular, predicted peak and valley (PaV) demand points seems critical, as they determine the maximum required generation capacity and baseload to meet the minimum underlying demand, respectively. In this paper, we propose multiple techniques to enhance day-ahead forecasting models by leveraging independent daily PaV predictors to ensemble short-term electricity demand forecasters. These ensemble techniques are then incorporated into a novel ensemble recommendation system (ERS). The ERS suggests the most appropriate ensemble technique to enhance the day-ahead predictor's performance while minimizing the computation required for testing multiple ensemble algorithms, relative to a single ensemble algorithm. This approach aims to improve the PaV forecasting and to enhance the overall accuracy of the day-ahead forecaster and it can be used with any combination of forecasting models. We demonstrate the effectiveness of our approach through a case study using a time-series prediction database model (tspDB) and a deep neural network (DNN) model for predicting the demand of the next day. The results show an improvement of 33% and 12% in the mean absolute percentage error of the forecasted PaV points using the tspDB and DNN models, respectively, as well as, enhancement in the overall day-ahead forecast.