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Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting
KTH, School of Electrical Engineering and Computer Science (EECS).
Tianjin Univ, Minist Educ, Key Lab Smart Grid, Tianjin 300072, Peoples R China..
Tianjin Univ, Minist Educ, Key Lab Smart Grid, Tianjin 300072, Peoples R China..
2018 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 11, no 8, article id 2163Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI , 2018. Vol. 11, no 8, article id 2163
Keywords [en]
photovoltaic power forecasting, GRU network, Pearson coefficient, K-means
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-238927DOI: 10.3390/en11082163ISI: 000446604100242Scopus ID: 2-s2.0-85052820872OAI: oai:DiVA.org:kth-238927DiVA, id: diva2:1263052
Note

QC 20181114

Available from: 2018-11-14 Created: 2018-11-14 Last updated: 2018-11-16Bibliographically approved

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Wang, Yusen

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Output format
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