A novel modeling approach for hourly forecasting of long-term electric energy demand


ENERGY CONVERSION AND MANAGEMENT, vol.52, no.1, pp.199-211, 2011 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 52 Issue: 1
  • Publication Date: 2011
  • Doi Number: 10.1016/j.enconman.2010.06.059
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.199-211
  • Keywords: Forecasting, Mathematical modeling, Surface fitting, Energy demand, WAVELET TRANSFORM, NEURAL-NETWORKS, LOAD, IMPLEMENTATION, VECTOR
  • Anadolu University Affiliated: Yes


In this study, a novel mathematical method is proposed for modeling and forecasting electric energy demand. The method is capable of making long-term forecasts. However, unlike other long-term forecasting models, the proposed method produces hourly results with improved accuracy. The model is constructed and verified using 26-year-long real-life load data (4 years with hourly resolution) obtained from the Turkish Electric Power Company. The overall method consists of a nested combination of three subsections for modeling. The first section is the coarse level for modeling variations of yearly average loads. The second section refines this structure by modeling weekly residual load variations within a year. The final section reaches to the hourly resolution by modeling variations within a week, using a novel 2-D mathematical representation at this resolution. The adoptions of such nested forecasting methodology together with the proposed 2-D representation for hourly load constitute the novelties of this work. The major advantage of the proposed approach is that it enables the possibility of making short-, medium-, and long-term hourly load forecasting within a single framework. Several mathematical functions are applied as models at each level of the nested system for achieving the minimal forecasting error. Proposed model functions with their corresponding forecasting accuracies are presented in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). (C) 2010 Elsevier Ltd. All rights reserved.