Sub-region division based short-term regional distributed PV power forecasting method considering spatio-temporal correlations

Wenzhe Lai, Zhao Zhen, Fei Wang, Wenjie Fu, Junlong Wang, Xudong Zhang and Hui Ren

Energy, 2024, vol. 288, issue C

Abstract: Accurate regional distributed PV power forecasting provides data support for power grid management and optimal operation. Distributed PV has the characteristics of large quantity, small capacity and difficulty in obtaining meteorological data. Statistical upscaling method is commonly used to forecast regional power. However, the current research ignores how to reasonably divide the sub-regions with similar output characteristics and mine the spatial and temporal correlation between different sub-regions. Therefore, this paper proposes a short-term regional distributed PV power forecasting method based on sub-region division considering spatio-temporal correlation. Firstly, the representative power plant is selected after dividing the sub-region by the AP clustering algorithm. Then, the GCN is used to extract spatial correlation features, and the LSTM is used to extract the evolution features of dynamic spatial correlation features, and the power forecasting models of representative plants in different weather types are established. Finally, the data integrity and similarity of the sub-region are scored, and the upscaling weight is determined to realize the power forecasting of the whole region. The distributed PV power generation data of Pingshan County, Hebei Province, China is used for simulation test. The results show that the forecasting method proposed has higher forecasting accuracy than the traditional model.

Keywords: Short-term; Regional PV power forecasting; Spatio-temporal correlation; Sub-region division (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:288:y:2024:i:c:s0360544223031109

DOI: 10.1016/j.energy.2023.129716

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