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

Author

Listed:
  • Yang, Hong Ying
  • Ye, Hao
  • Wang, Guizeng
  • Khan, Junaid
  • Hu, Tongfu

Abstract

This paper presents an improved fuzzy neural system (FNS) for electric very-short-term load forecasting problem based on chaotic dynamics reconstruction technique. The Grassberger–Procaccia algorithm and least squares regression method are applied to obtain the value of correlation dimension for estimation of the model order. Based on this order, an appropriately structured FNS model is designed for the prediction of electric load. In order to reduce the practical influences of the computation error on correlation dimension estimation, a dimension switching detector is devised to enhance the prediction performance of the FNS. Satisfactory experimental results are obtained for 15min ahead forecasting by using actual load data of Shandong Heze Electric Utility, China. To have a comparison with the proposed approach, similar experiments using conventional artificial neural network (ANN) are also performed.

Suggested Citation

  • Yang, Hong Ying & Ye, Hao & Wang, Guizeng & Khan, Junaid & Hu, Tongfu, 2006. "Fuzzy neural very-short-term load forecasting based on chaotic dynamics reconstruction," Chaos, Solitons & Fractals, Elsevier, vol. 29(2), pages 462-469.
    • Handle: RePEc:eee:chsofr:v:29:y:2006:i:2:p:462-469
    DOI: 10.1016/j.chaos.2005.08.095
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    Cited by:

    1. Yasir Alsaedi & Gurudeo Anand Tularam & Victor Wong, 2019. "Application of ARIMA Modelling for the Forecasting of Solar, Wind, Spot and Options Electricity Prices: The Australian National Electricity Market," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 263-272.
    2. Mesbaholdin Salami & Farzad Movahedi Sobhani & Mohammad Sadegh Ghazizadeh, 2018. "Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market," Data, MDPI, vol. 3(4), pages 1-26, October.
    3. Guégan, Dominique & Leroux, Justin, 2009. "Forecasting chaotic systems: The role of local Lyapunov exponents," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2401-2404.
    4. Wang, Jianzhou & Jia, Ruiling & Zhao, Weigang & Wu, Jie & Dong, Yao, 2012. "Application of the largest Lyapunov exponent and non-linear fractal extrapolation algorithm to short-term load forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 45(9), pages 1277-1287.
    5. Sangiorgio, Matteo & Dercole, Fabio, 2020. "Robustness of LSTM neural networks for multi-step forecasting of chaotic time series," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    6. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
    7. Guerra, Fábio A. & Coelho, Leandro dos S., 2008. "Multi-step ahead nonlinear identification of Lorenz’s chaotic system using radial basis neural network with learning by clustering and particle swarm optimization," Chaos, Solitons & Fractals, Elsevier, vol. 35(5), pages 967-979.

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