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Open AccessArticle

Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting

1
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden
2
Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
*
Author to whom correspondence should be addressed.
Energies 2018, 11(8), 2163; https://doi.org/10.3390/en11082163
Received: 19 July 2018 / Revised: 8 August 2018 / Accepted: 15 August 2018 / Published: 18 August 2018
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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. View Full-Text
Keywords: photovoltaic power forecasting; GRU network; Pearson coefficient; K-means photovoltaic power forecasting; GRU network; Pearson coefficient; K-means
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Wang, Y.; Liao, W.; Chang, Y. Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting. Energies 2018, 11, 2163.

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