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Probabilistic Forecasting of Short-Term Electric Load Demand: An Integration Scheme Based on Correlation Analysis and Improved Weighted Extreme Learning Machine

School of Electrical Engineering and Automation; Wuhan University, Wuhan 430072, China
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Appl. Sci. 2019, 9(20), 4215; https://doi.org/10.3390/app9204215
Received: 28 August 2019 / Revised: 25 September 2019 / Accepted: 2 October 2019 / Published: 10 October 2019
(This article belongs to the Special Issue Machine Learning for Energy Forecasting)
Precise prediction of short-term electric load demand is the key for developing power market strategies. Due to the dynamic environment of short-term load forecasting, probabilistic forecasting has become the center of attention for its ability of representing uncertainty. In this paper, an integration scheme mainly composed of correlation analysis and improved weighted extreme learning machine is proposed for probabilistic load forecasting. In this scheme, a novel cooperation of wavelet packet transform and correlation analysis is developed to deal with the data noise. Meanwhile, an improved weighted extreme learning machine with a new switch algorithm is provided to effectively obtain stable forecasting results. The probabilistic forecasting task is then accomplished by generating the confidence intervals with the Gaussian process. The proposed integration scheme, tested by actual data from Global Energy Forecasting Competition, is proved to have a better performance in graphic and numerical results than the other available methods. View Full-Text
Keywords: probabilistic forecasting; integration scheme; correlation analysis; improved weighted extreme learning machine; switch algorithm; Gaussian process probabilistic forecasting; integration scheme; correlation analysis; improved weighted extreme learning machine; switch algorithm; Gaussian process
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Kong, Z.; Xia, Z.; Cui, Y.; Lv, H. Probabilistic Forecasting of Short-Term Electric Load Demand: An Integration Scheme Based on Correlation Analysis and Improved Weighted Extreme Learning Machine. Appl. Sci. 2019, 9, 4215.

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