Daily Peak Load Forecasting by a Correntropy based Artificial Neural Network using an Adaptive Kernel Size Method Considering Outliers

Author:

Sakurai Daiji1,Fukuyama Yoshikazu1,Iizaka Tatsuya2,Matsui Tetsuro2

Affiliation:

1. Meiji University

2. Fuji Electric, Co. Ltd.

Publisher

Institute of Electrical Engineers of Japan (IEE Japan)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference26 articles.

1. (1) T. Haida and S. Muto: “Regression based peak load forecasting using a transformation technique”, IEEE Trans. Power Syst., Vol. 9, No. 4, pp. 1788-1794 (1994)

2. (3) L. Jin, Y. J. Lai, and T. X. Long: “Peak Load Forecasting Based on Robust Regression Model”, IEEE 8th International Conference on Probabilistic Methods Applied to Power Systems, pp. 123-128 (2004)

3. (4) F. A. Razak, A. H. Hashim, and I. Z. Abidin: “Malaysian peak daily load forecasting”, Proceedings of 2009 IEEE Student Conference on Reseach and Development (SCOReD 2009), pp. 392-394 (2009)

4. (5) Y. Y. Hsu and C. C. Yang: “Design of artificial neural networks for short-term load forecasting. II. Multilayer feedforward networks for peak load and valley load forecasting”, IEE Proc. C, Generation, Transmission and Distribution, Vol. 138, No. 5, pp. 414-418 (1991)

5. (6) T. Onoda: “Next day peak load forecasting using an artificial neural network”, Proc. of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya), pp. 2029-2032, Nagoya, Japan (1993)

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