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Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function

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  • He, Yaoyao
  • Xu, Qifa
  • Wan, Jinhong
  • Yang, Shanlin

Abstract

Highly accurate short-term power load forecasting (STLF) is fundamental to the success of reducing the risk when making power system planning and operational decisions. For quantifying uncertainty associated with power load and obtaining more information of future load, a probability density forecasting method based on quantile regression neural network using triangle kernel function (QRNNT) is proposed. The nonlinear structure of neural network is applied to transform the quantile regression model for constructing probabilistic forecasting method. Moreover, the triangle kernel function and direct plug-in bandwidth selection method are employed to perform kernel density estimation. To verify the efficiency, the proposed method is used for Canada's and China's load forecasting. The complete probability density curves are obtained to indicate the QRNNT method is capable of forecasting high quality prediction interval (PIs) with higher coverage probability. Numerical results also confirm favorable performance of proposed method in comparison with the several existing forecasting methods.

Suggested Citation

  • He, Yaoyao & Xu, Qifa & Wan, Jinhong & Yang, Shanlin, 2016. "Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function," Energy, Elsevier, vol. 114(C), pages 498-512.
    • Handle: RePEc:eee:energy:v:114:y:2016:i:c:p:498-512
    DOI: 10.1016/j.energy.2016.08.023
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    21. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
    22. He, Yaoyao & Liu, Rui & Li, Haiyan & Wang, Shuo & Lu, Xiaofen, 2017. "Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory," Applied Energy, Elsevier, vol. 185(P1), pages 254-266.
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    24. He, Yaoyao & Qin, Yang & Wang, Shuo & Wang, Xu & Wang, Chao, 2019. "Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network," Applied Energy, Elsevier, vol. 233, pages 565-575.
    25. Zhang, Shu & Wang, Yi & Zhang, Yutian & Wang, Dan & Zhang, Ning, 2020. "Load probability density forecasting by transforming and combining quantile forecasts," Applied Energy, Elsevier, vol. 277(C).

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