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Regular Paper | Open Access

Very Short-term Forecasting of Distributed PV Power Using GSTANN

Tiechui Yao1,2, Jue Wang1,2( ), Yangang Wang1,2, Pei Zhang3, Haizhou Cao1,2, Xuebin Chi1,2, Min Shi4
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 University, Beijing 100044, China
State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China
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Abstract

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 challenges are encountered in distributed PV power forecasting: 1) lack of on-site meteorological observation, 2) leveraging extraneous 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

Keywords

Distributed photovoltaic power forecastinggraph convolutional networkssatellite imagesspatial-temporal attention

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CSEE Journal of Power and Energy Systems
Volume 10 Issue 4,
July 2024
Pages 1491-1501
DOI: 10.17775/CSEEJPES.2022.00110
Cite this article:
Yao T, Wang J, Wang Y, et al. Very Short-term Forecasting of Distributed PV Power Using GSTANN. CSEE Journal of Power and Energy Systems, 2024, 10(4): 1491-1501. https://doi.org/10.17775/CSEEJPES.2022.00110

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Received: 05 January 2022
Revised: 28 April 2022
Accepted: 27 May 2022
Published: 12 October 2022
© 2022 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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