SAGA-FCM-LSSVM model-based short-term power forecasting of photovoltaic power plants - Details

author：

Peng, Z.N. (Peng, Z.N..) ^[1] | Lin, P.J. (Lin, P.J..) ^[2] (Scholars：林培杰) | Lai, Y.F. (Lai, Y.F..) ^[3] (Scholars：赖云锋) | Chen, Z.C. (Chen, Z.C..) ^[4] (Scholars：陈志聪) | Wu, L.J. (Wu, L.J..) ^[5] (Scholars：吴丽君) | Cheng, S.Y. (Cheng, S.Y..) ^[6] (Scholars：程树英) Unfold

Indexed by：

EI Scopus

Abstract：

With　the　rapid　development　of　social　economy,　vigorously　developing　solar　energy　has　become　a　powerful　means　to　solve　energy　and　environmental　problems.　However,　the　instability　of　weather　condition　makes　the　output　of　PV　power　have　strong　randomness,　fluctuations　and　intermittence.　Thus　accurate　photovoltaic　(PV)　power　forecast　eliminates　the　negative　impacts　of　the　grid　connection　of　PV　power　generation　systems,　which　are　very　meaningful　for　effectively　integrating　the　PV　power　systems　into　the　grid.　The　paper　presents　a　Simulate　Anneal　and　Genetic　Algorithm　(SAGA),　fuzzy　c-means　clustering　(FCM)　and　least　square　support　vector　machine　(LSSVM)　(SAGA-FCM-LSSVM)　model-based　power　short-term　forecasting　of　PV　power　plants　approach.　The　experimental　effect　of　the　proposed　prediction　method　is　verified　by　employing　large　datasets　from　the　Desert　Knowledge　Australia　Solar　Center　(DKASC)　website.　In　this　work,　the　FCM　clustering　algorithm　is　adopted　to　cluster　the　historical　power　datasets,　and　the　LSSVM　technique　maps　the　multivariate　meteorological　factors　and　power　data　nonlinear　relationship.　The　SAGA　method　is　applied　to　improve　the　initial　clustering　centers　of　the　FCM　clustering　algorithm　to　obtain　a　higher　prediction　performance.　The　prediction　result　of　the　method　in　this　paper　is　contrasted　with　back　propagation　neural　network　(BPNN)　and　LSSVM　models,　and　reveals　excellent　effect　in　improving　the　accuracy　of　prediction.　©　Published　under　licence　by　IOP　Publishing　Ltd.

Keyword：

Backpropagation Clustering algorithms Forecasting Genetic algorithms Large dataset Neural networks Photovoltaic cells Power plants Solar energy Solar power generation Support vector machines

Community：

[ 1 ] [Peng, Z.N.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou; 350116, China
[ 2 ] [Peng, Z.N.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou; 213164, China
[ 3 ] [Lin, P.J.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou; 350116, China
[ 4 ] [Lin, P.J.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou; 213164, China
[ 5 ] [Lai, Y.F.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou; 350116, China
[ 6 ] [Lai, Y.F.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou; 213164, China
[ 7 ] [Chen, Z.C.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou; 350116, China
[ 8 ] [Chen, Z.C.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou; 213164, China
[ 9 ] [Wu, L.J.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou; 350116, China
[ 10 ] [Wu, L.J.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou; 213164, China
[ 11 ] [Cheng, S.Y.]College of Physics and Information Engineering, And Institute of Micro-Nano Devices and Solar Cells, Fuzhou University, Fuzhou; 350116, China
[ 12 ] [Cheng, S.Y.]Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering, Changzhou; 213164, China