Medium- and Long-Term Distributed Photovoltaic Power Prediction Based on Multiple Time Series Feature and Multiple-Model Fusion

Xinting Yang, Shengyong Ye, Keteng Jiang (), Chongbo Sun, Zongxiang Lu, Liyang Liu, Yuqi Han and Bin Zhang
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Xinting Yang: State Grid Sichuan Electric Power Company, Economic and Technological Research Institute, Chengdu 610000, China
Shengyong Ye: State Grid Sichuan Electric Power Company, Economic and Technological Research Institute, Chengdu 610000, China
Keteng Jiang: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Chongbo Sun: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China
Zongxiang Lu: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Liyang Liu: State Grid Sichuan Electric Power Company, Economic and Technological Research Institute, Chengdu 610000, China
Yuqi Han: State Grid Sichuan Electric Power Company, Economic and Technological Research Institute, Chengdu 610000, China
Bin Zhang: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China

Sustainability, 2024, vol. 16, issue 20, 1-16

Abstract: Distributed photovoltaic power stations have advantages such as local direct power supply and reduced transmission energy consumption, and whose demands are constantly being developed. Conducting research on medium- and long-term distributed photovoltaic prediction will have significant value for applications such as the electricity trade market, power grid operation, and the planning of new power stations. Due to characteristics such as long time dependence, disperse power stations, and strong randomness, making accurate and stable predictions becomes very difficult. In this research, we propose a multiple time series feature and multiple-model fusion-based ensemble learning model for medium- and long-term distributed photovoltaic power prediction (M2E-DPV). Considering the wave influence and the differences in distributions in different areas of photovoltaic power, multiple feature combinations are designed to increase feature expression ability and adaptability. Based on the boost ensemble learning model, trained on a single model of different time scale features, the optimal scoring strategy is used for multiple model fusion in the rolling prediction process, and finally, time-segmented probabilistic correction is performed. The experiment results show the effectiveness of the M2E-DPV under multiple feature combinations and multi-model fusion strategies. The average MAPE, R 2 , and ACC indicators are 0.15, 0.96, and 0.91, respectively. Compared with other methods, there is a significant improvement, indicating that the prediction ability of the model framework proposed in this paper is advanced and robust.

Keywords: distributed photovoltaic; medium- and long-term power prediction; multi-model fusion; weather factors; probabilistic correction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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