Very-short-term power prediction for PV power plants using a simple and effective RCC-LSTM model based on short term multivariate historical datasets - Details

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Abstract：

Improving　the　accuracy　of　very-short-term　(VST)　photovoltaic　(PV)　power　generation　prediction　can　effectively　enhance　the　quality　of　operational　scheduling　of　PV　power　plants,　and　provide　a　reference　for　PV　maintenance　and　emergency　response.　In　this　paper,　the　effects　of　different　meteorological　factors　on　PV　power　generation　as　well　as　the　degree　of　impact　at　different　time　periods　are　analyzed.　Secondly,　according　to　the　characteristics　of　radiation　coordinate,　a　simple　radiation　classification　coordinate　(RCC)　method　is　proposed　to　classify　and　select　similar　time　periods.　Based　on　the　characteristics　of　PV　power　time-series,　the　selected　similar　time　period　dataset　(include　power　output　and　multivariate　meteorological　factors　data)　is　reconstructed　as　the　training　dataset.　Then,　the　long　short-term　memory　(LSTM)　recurrent　neural　network　is　applied　as　the　learning　network　of　the　proposed　model.　The　proposed　model　is　tested　on　two　independent　PV　systems　from　the　Desert　Knowledge　Australia　Solar　Centre　(DKASC)　PV　data.　The　proposed　model　achieving　mean　absolute　percentage　error　of　2.74–7.25%,　and　according　to　four　error　metrics,　the　results　show　that　the　robustness　and　accuracy　of　the　RCC-LSTM　model　are　better　than　the　other　four　comparison　models.　©　2020　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.

Keyword：

Long short-term memory; Photovoltaic power generation; Similarity time period; Very short-term Power prediction

Community：

[ 1 ] [Chen, B.]School of Physics and Information Engineering, and Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, 350108, China
[ 2 ] [Chen, B.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, 21316, China
[ 3 ] [Lin, P.]School of Physics and Information Engineering, and Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, 350108, China
[ 4 ] [Lin, P.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, 21316, China
[ 5 ] [Lai, Y.]School of Physics and Information Engineering, and Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, 350108, China
[ 6 ] [Lai, Y.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, 21316, China
[ 7 ] [Cheng, S.]School of Physics and Information Engineering, and Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, 350108, China
[ 8 ] [Cheng, S.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, 21316, China
[ 9 ] [Chen, Z.]School of Physics and Information Engineering, and Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, 350108, China
[ 10 ] [Chen, Z.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, 21316, China
[ 11 ] [Wu, L.]School of Physics and Information Engineering, and Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, 350108, China
[ 12 ] [Wu, L.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, 21316, China

Reprint 's Address：

[Lin, P.]School of Physics and Information Engineering, and Institute of Micro-Nano Devices and Solar Cells, Fuzhou UniversityChina

Email：

linpeijie@fzu.edu.cn

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Source ：

Electronics (Switzerland)

ISSN： 2079-9292

Year： 2020

Issue： 2

Volume： 9

1 . 7 6 4

JCR@2018

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SCOPUS Cited Count： 64

ESI Highly Cited Papers on the List： 0 Unfold All

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Chinese Cited Count：

30 Days PV： 2

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