Short-Term Photovoltaic Power Probabilistic Forecasting Based on Temporal Decomposition and Vine Copula

Xinghua Wang, Zilv Li (), Chenyang Fu, Xixian Liu, Weikang Yang, Xiangyuan Huang, Longfa Yang, Jianhui Wu and Zhuoli Zhao
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Xinghua Wang: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Zilv Li: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Chenyang Fu: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Xixian Liu: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Weikang Yang: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Xiangyuan Huang: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Longfa Yang: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Jianhui Wu: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Zhuoli Zhao: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China

Sustainability, 2024, vol. 16, issue 19, 1-25

Abstract: With the large-scale development of solar power generation, highly uncertain photovoltaic (PV) power output has an increasing impact on distribution networks. PV power generation has complex correlations with various weather factors, while the time series embodies multiple temporal characteristics. To more accurately quantify the uncertainty of PV power generation, this paper proposes a short-term PV power probabilistic forecasting method based on the combination of decomposition prediction and multidimensional variable dependency modeling. First, a seasonal and trend decomposition using a Loess (STL)-based PV time series feature decomposition model is constructed to obtain periodic, trend, and residual components representing different characteristics. For different components, this paper develops a periodic component prediction model based on TimeMixer for multi-scale temporal feature mixing, a long short-term memory (LSTM)-based trend component extraction and prediction model, and a multidimensional PV residual probability density prediction model optimized by Vine Copula optimized with Q-Learning. These components’ results form a short-term PV probabilistic forecasting method that considers both temporal features and multidimensional variable correlations. Experimentation with data from the Desert Knowledge Australia Solar Center (DKASC) demonstrates that the proposed method reduced root mean square error (RMSE) and mean absolute percentage error (MAPE) by at least 14.8% and 22%, respectively, compared to recent benchmark models. In probability interval prediction, while improving accuracy by 4% at a 95% confidence interval, the interval width decreased by 19%. The results show that the proposed approach has stronger adaptability and higher accuracy, which can provide more valuable references for power grid planning and decision support.

Keywords: time series decomposition; probabilistic forecasting; dependency model; photovoltaic power forecasting; TimeMixer; Vine Copula; Q-Learning (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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