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Short-Term Load Forecasting Using Generalized Regression and Probabilistic Neural Networks in the Electricity Market

Author

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  • Tripathi, M.M.
  • Upadhyay, K.G.
  • Singh, S.N.

Abstract

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Suggested Citation

  • Tripathi, M.M. & Upadhyay, K.G. & Singh, S.N., 2008. "Short-Term Load Forecasting Using Generalized Regression and Probabilistic Neural Networks in the Electricity Market," The Electricity Journal, Elsevier, vol. 21(9), pages 24-34, November.
    • Handle: RePEc:eee:jelect:v:21:y:2008:i:9:p:24-34
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    Cited by:

    1. Tongxiang Liu & Yu Jin & Yuyang Gao, 2019. "A New Hybrid Approach for Short-Term Electric Load Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Whale Optimization," Energies, MDPI, vol. 12(8), pages 1-20, April.
    2. Khan, Ahsan Raza & Mahmood, Anzar & Safdar, Awais & Khan, Zafar A. & Khan, Naveed Ahmed, 2016. "Load forecasting, dynamic pricing and DSM in smart grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1311-1322.
    3. Zhaorui Meng & Xianze Xu, 2019. "A Hybrid Short-Term Load Forecasting Framework with an Attention-Based Encoder–Decoder Network Based on Seasonal and Trend Adjustment," Energies, MDPI, vol. 12(24), pages 1-14, December.
    4. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    5. Raneen Younis & Andreas Reinhardt, 2020. "A Study on Fundamental Waveform Shapes in Microscopic Electrical Load Signatures," Energies, MDPI, vol. 13(12), pages 1-19, June.

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