Accurate Electricity Load Forecasting with Artificial Neural Networks

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.

• Type: Conference
• Year: 2005
• Where: CIMCA
• Authors: Daniel Ortiz Arroyo, Morten K. Skov, Quang Huynh
• Keywords: ANN-based Prediction Model, Artificial Neural Networks, CIMCA 2005, Feed-forward Ann-based Model, Intelligent Agents