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Improved Probability Prediction Method Research for Photovoltaic Power Output

School of Electrical Engineering and Automation, Tianjin University, Tianjin 300000, China
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Appl. Sci. 2019, 9(10), 2043; https://doi.org/10.3390/app9102043
Received: 5 April 2019 / Revised: 5 May 2019 / Accepted: 8 May 2019 / Published: 17 May 2019
(This article belongs to the Special Issue Cutting-Edge Technologies for Renewable Energy Production and Storage)
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Abstract

Due to solar radiation and other meteorological factors, photovoltaic (PV) output is intermittent and random. Accurate and reliable photovoltaic power prediction can improve the stability and safety of grid operation. Compared to solar power point prediction, probabilistic prediction methods can provide more information about potential uncertainty. Therefore, this paper first proposes two kinds of photovoltaic output probability prediction models, which are improved sparse Gaussian process regression model (IMSPGP), and improved least squares support vector machine error prediction model (IMLSSVM). In order to make full use of the advantages of the different models, this paper proposes a combined forecasting method with divided-interval and variable weights, which divides one day into four intervals. The models are combined by the optimal combination method in each interval. The simulation results show that IMSPGP and IMLSSVM have better prediction accuracy than the original models, and the combination model obtained by the combination method proposed in this paper further improves the prediction performance. View Full-Text
Keywords: PV; probability prediction; sparse Gaussian process regression; least squares support vector machine; combination method PV; probability prediction; sparse Gaussian process regression; least squares support vector machine; combination method
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Cheng, Z.; Liu, Q.; Zhang, W. Improved Probability Prediction Method Research for Photovoltaic Power Output. Appl. Sci. 2019, 9, 2043.

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