Accurate Electricity Load Forecasting with Artificial Neural Networks

  • Authors:
  • Daniel Ortiz-Arroyo;Morten K. Skov;Quang Huynh

  • Affiliations:
  • Computer Science Department, Aalborg University Esbjerg Denmark;Computer Science Department, Aalborg University Esbjerg Denmark;Computer Science Department, Aalborg University Esbjerg Denmark

  • Venue:
  • CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
  • Year:
  • 2005

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Abstract

In this paper we present a simple yet accurate model to forecast electricity load with Artificial Neural Networks (ANNs). We analyze the problem domain and choose the most adequate set of attributes in our model. To obtain the best performance in prediction, we follow an experimental approach analyzing the entire ANN design space and applying different training strategies. We found that when little data is available, applying this approach is critical to obtain the best results. Our experiments also show that a simple ANN-based prediction model appropriately tuned can outperform other more complex models. Our feed-forward ANN-based model obtained 29% improvement in prediction accuracy when compared to the best results presented in the 2001 EUNITE competition.