Short-Term load forecasting based on mutual information and artificial neural network

Authors:
Zhiyong Wang;Yijia Cao
Affiliations:
College of Electrical Engineering, Zhejiang University, Hangzhou, China;College of Electrical Engineering, Zhejiang University, Hangzhou, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Neural Computation

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Abstract

Short term load forecasting (STLF) has an essential role in the operation of electric power systems. Although artificial neural networks (ANN) based predictors are more widely used for STLF in recent years, there still exist some difficulties in choosing the proper input variables and selecting an appropriate architecture of the networks. A novel approach is proposed for STLF by combining mutual information (MI) and ANN. The MI theory is first briefly introduced and employed to perform input selection and determine the initial weights of ANN. Then ANN module is trained using historical daily load and weather data selected to perform the final forecast. To demonstrate the effectiveness of the approach, short-term load forecasting was performed on the Hang Zhou Electric Power Company in China, and the testing results show that the proposed model is feasible and promising for load forecasting.