Power Prediction of Regional Photovoltaic Power Stations Based on Meteorological Encryption and Spatio-Temporal Graph Networks

Shunli Deng, Shuangxi Cui () and Anchen Xu
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Shunli Deng: School of Electrical Engineering, Xinjiang University, Urumqi 830047, China
Shuangxi Cui: School of Electrical Engineering, Xinjiang University, Urumqi 830047, China
Anchen Xu: School of Electrical Engineering, Xinjiang University, Urumqi 830047, China

Energies, 2024, vol. 17, issue 14, 1-22

Abstract: Distributed photovoltaic (PV) power stations generally lack historical meteorological data, which is one of the main reasons for their insufficient power prediction accuracy. To address this issue, this paper proposes a power prediction method for regional distributed PV power stations based on meteorological encryption and spatio-temporal graph networks. First, inverse distance weighted meteorological encryption technology is used to achieve the comprehensive coverage of key meteorological resources based on the geographical locations of PV power stations and the meteorological resources of weather stations. Next, the historical power correlations between PV power stations are analyzed, and highly correlated stations are connected to construct a topological graph structure. Then, an improved spatio-temporal graph network model is established based on this graph to deeply mine the spatio-temporal characteristics of regional PV power stations. Furthermore, a dual-layer attention mechanism is added to further learn the feature attributes of nodes and enhance the spatio-temporal features extracted by the spatio-temporal graph network, ultimately achieving power prediction for regional PV power stations. The simulation results indicate that the proposed model demonstrates excellent prediction accuracy, robustness, extensive generalization capability, and broad applicability.

Keywords: regional photovoltaic power prediction; meteorological encryption; spatio-temporal graph networks; dual-layer attention mechanism (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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