Informer Short-Term PV Power Prediction Based on Sparrow Search Algorithm Optimised Variational Mode Decomposition

Wu Xu, Dongyang Li, Wenjing Dai and Qingchang Wu ()
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Wu Xu: School of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China
Dongyang Li: School of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China
Wenjing Dai: School of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China
Qingchang Wu: Lancang–Mekong International Vocational Institute, Yunnan Minzu University, Kunming 650504, China

Energies, 2024, vol. 17, issue 12, 1-16

Abstract: The output power of PV systems is influenced by various factors, resulting in strong volatility and randomness, which makes it difficult to forecast. Therefore, this paper proposes an Informer prediction model based on optimised VMD for predicting short-term PV power. Firstly, the temporal coding of the Informer model is improved and, secondly, the original sequence is decomposed into multiple modal components using VMD, and then optimisation of the results of VMD in conjunction with the optimisation strategy of SSA improves the characteristics of the time series data. Finally, the refined data are fed into the Informer framework for modelling and prediction, utilising the self-attention mechanism and multiscale feature fusion of Informer to precisely forecast PV power. The power of PV prediction data from the SSA-VMD-Informer model and four other commonly used models is compared. Experimental results indicate that the SSA-VMD-Informer model performs exceptionally well in short-term PV power prediction, achieving higher accuracy than traditional methods. As an example, the results of predicting the PV power on 24 April in a region of Xinjiang are 1.3882 for RMSE, 0.8310 for MSE, 1.14 for SDE, and 0.9944 for R 2 .

Keywords: PV power; time series; Informer; VMD; SSA; short-term forecasting (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 complete reference list from CitEc
Citations: View citations in EconPapers (1)

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