Fuzzy neural very-short-term load forecasting based on chaotic dynamics reconstruction

Authors:
Hongying Yang;Hao Ye;Guizeng Wang;Tongfu Hu
Affiliations:
Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

Citing 2
Cited 0

Neural Short-Term Prediction Based on Dynamics Reconstruction

Neural Processing Letters
Computational Intelligence Techniques for Short-Term Electric Load Forecasting

Journal of Intelligent and Robotic Systems

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Abstract

This paper proposes an improved fuzzy neural system for very-short-term load forecasting problem, based on chaotic dynamics reconstruction techniques. The Grassberger–Procaccia algorithm and the Least Squares Regression method were applied to obtain the accurate value for the correlation dimension, which is used as an estimation of the model order. Based on that model order, an appropriately structured Fuzzy Neural System (FNS) for load forecasting was designed. Satisfactory experimental results were obtained in 15 minutes ahead electrical load forecasting from the electric utility in Shandong Heze area. And the same experiments using conventional Artificial Neural Network are also performed as a comparison with the proposed approach.