首页|Very Short-term Forecasting of Distributed PV Power Using GSTANN
Very Short-term Forecasting of Distributed PV Power Using GSTANN
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Photovoltaic(PV)power forecasting is essential for secure operation of a power system.Effective prediction of PV power can improve new energy consumption capacity,help power system planning,promote development of smart grids,and ultimately support construction of smart energy cities.However,different from centralized PV power forecasts,three critical chal-lenges are encountered in distributed PV power forecasting:1)lack of on-site meteorological observation,2)leveraging extrane-ous data to enhance forecasting performance,3)spatial-temporal modelling methods of meteorological information around the distributed PV stations.To address these issues,we propose a Graph Spatial-Temporal Attention Neural Network(GSTANN)to predict the very short-term power of distributed PV.First,we use satellite remote sensing data covering a specific geographical area to supplement meteorological information for all PV stations.Then,we apply the graph convolution block to model the non-Euclidean local and global spatial dependence and design an attention mechanism to simultaneously derive temporal and spatial correlations.Subsequently,we propose a data fusion module to solve the time misalignment between satellite remote sensing data and surrounding measured on-site data and design a power approximation block to map the conversion from solar irradiance to PV power.Experiments conducted with real-world case study datasets demonstrate that the prediction performance of GSTANN outperforms five state-of-the-art baselines.
Distributed photovoltaic power forecastinggraph convolutional networkssatellite imagesspatial-temporal attention
Tiechui Yao、Jue Wang、Yangang Wang、Pei Zhang、Haizhou Cao、Xuebin Chi、Min Shi
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Computer Network Information Center of the Chinese Academy of Sciences,Beijing 100190,China
University of Chinese Academy of Sciences,Beijing 100190,China
School of Electrical Engineering,Beijing Jiaotong Uni-versity,Beijing 100044,China
State Grid Hebei Electric Power Co.,Ltd.,Shijiazhuang 050000,China
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Strategic Priority Research Program of the Chinese Academy of Sciences
维普
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