Hour-ahead photovoltaic power forecast using a hybrid GRA-LSTM model based on multivariate meteorological factors and historical power datasets - Details

author：

Chen, Biaowei (Chen, Biaowei.) ^[1] | Lin, Peijie (Lin, Peijie.) ^[2] (Scholars：林培杰) | Lin, Yaohai (Lin, Yaohai.) ^[3] | Lai, Yunfeng (Lai, Yunfeng.) ^[4] (Scholars：赖云锋) | Cheng, Shuying (Cheng, Shuying.) ^[5] (Scholars：程树英) | Chen, Zhicong (Chen, Zhicong.) ^[6] (Scholars：陈志聪) | Wu, Lijun (Wu, Lijun.) ^[7] (Scholars：吴丽君)

Indexed by：

EI Scopus

Abstract：

Owing　to　the　clean,　inexhaustible　and　pollution-free,　solar　energy　has　become　a　powerful　means　to　solve　energy　and　environmental　problems.　However,　photovoltaic　(PV)　power　generation　varies　randomly　and　intermittently　with　respect　to　the　weather,　which　bring　the　challenge　to　the　dispatching　of　PV　electrical　power.　Thus,　power　forecasting　for　PV　power　generation　has　become　one　of　the　key　basic　technologies　to　overcome　this　challenge.　The　paper　presents　a　grey　relational　analysis　(GRA)　and　long　short-term　memory　recurrent　neural　network　(LSTM　RNN)　(GRA-LSTM)　model-based　power　short-term　forecasting　of　PV　power　plants　approach.　The　GRA　algorithm　is　adopted　to　select　the　similar　hours　from　history　dataset,　and　then　the　LSTM　NN　maps　the　nonlinear　relationship　between　the　multivariate　meteorological　factors　and　power　data.　The　proposed　model　is　verified　by　using　the　dataset　of　the　PV　systems　from　the　Desert　Knowledge　Australia　Solar　Center　(DKASC).　The　prediction　results　of　the　method　are　contrasted　with　those　obtained　by　LSTM,　grey　relational　analysis-back　propagation　neural　network　(GRA-BPNN),　grey　relational　analysis-radial　basis　function　neural　network　(GRA-RBFNN)　and　grey　relational　analysis-Elman　neural　network　(GRA-Elman),　respectively.　Results　show　an　acceptable　and　robust　performance　of　the　proposed　model.　©　2020　IOP　Publishing　Ltd.　All　rights　reserved.

Keyword：

Backpropagation Forecasting Long short-term memory Photovoltaic cells Radial basis function networks Solar energy

Community：

[ 1 ] [Chen, Biaowei]School of Physics and Information Engineering, Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, China
[ 2 ] [Chen, Biaowei]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, China
[ 3 ] [Lin, Peijie]School of Physics and Information Engineering, Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, China
[ 4 ] [Lin, Peijie]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, China
[ 5 ] [Lin, Yaohai]College of Computer and Information Sciences, Fujian Agriculture and Forest University, Fuzhou, China
[ 6 ] [Lai, Yunfeng]School of Physics and Information Engineering, Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, China
[ 7 ] [Lai, Yunfeng]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, China
[ 8 ] [Cheng, Shuying]School of Physics and Information Engineering, Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, China
[ 9 ] [Cheng, Shuying]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, China
[ 10 ] [Chen, Zhicong]School of Physics and Information Engineering, Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, China
[ 11 ] [Chen, Zhicong]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, China
[ 12 ] [Wu, Lijun]School of Physics and Information Engineering, Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou, China
[ 13 ] [Wu, Lijun]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou, China