LAPSE

LAPSE:2018.0684
Published Article
LAPSE:2018.0684
Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting
Yusen Wang, Wenlong Liao, Yuqing Chang
September 21, 2018
Photovoltaic power has great volatility and intermittency due to environmental factors. Forecasting photovoltaic power is of great significance to ensure the safe and economical operation of distribution network. This paper proposes a novel approach to forecast short-term photovoltaic power based on a gated recurrent unit (GRU) network. Firstly, the Pearson coefficient is used to extract the main features that affect photovoltaic power output at the next moment, and qualitatively analyze the relationship between the historical photovoltaic power and the future photovoltaic power output. Secondly, the K-means method is utilized to divide training sets into several groups based on the similarities of each feature, and then GRU network training is applied to each group. The output of each GRU network is averaged to obtain the photovoltaic power output at the next moment. The case study shows that the proposed approach can effectively consider the influence of features and historical photovoltaic power on the future photovoltaic power output, and has higher accuracy than the traditional methods.
Keywords
GRU network, K-means, Pearson coefficient, photovoltaic power forecasting
Suggested Citation
Wang Y, Liao W, Chang Y. Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting. (2018). LAPSE:2018.0684
Author Affiliations
Wang Y: School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden
Liao W: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Chang Y: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
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Journal Name
Energies
Volume
11
Issue
8
Article Number
E2163
Year
2018
Publication Date
2018-08-18
Published Version
ISSN
1996-1073
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Original Submission
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PII: en11082163, Publication Type: Journal Article
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LAPSE:2018.0684
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doi:10.3390/en11082163
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Sep 21, 2018
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Sep 21, 2018
 
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Sep 21, 2018
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Original Submitter
Calvin Tsay
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