Short-Term Load Forecasting Using Broad Learning System

摘要

Short-term load forecasting (STLF) is of great value in power system operation. This paper proposes an STLF method based on the broad learning system (BLS). First, factors that have major impacts on load demands are selected as input variables of STLF. Then, the construction of BLS is discussed. The original input is mapped into feature nodes using the sparse autoencoder, and the network expands in width through the enhancement nodes which are generated from the mapped features. Both the feature and enhancement nodes are directly connected to the output layer. Such a flat structure is exploited to implement incremental learning, which updates BLS dynamically without retraining. Finally, the proposed model is tested on the ISO New England dataset. Test results indicate that BLS is accurate and efficient compared with the existing machine learning techniques. Moreover, the incremental learning capability improves the efficiency of the update of STLF models remarkably.