Deep neural network for forecasting of photovoltaic power based on wavelet packet decomposition with similar day analysis

Xiangjie Liu, Yuanyan Liu, Xiaobing Kong, Lele Ma, Ahmad H. Besheer and Kwang Y. Lee

Energy, 2023, vol. 271, issue C

Abstract: Photovoltaic (PV) power forecasting (PVPF) plays an important role in the scheduling and operation of modern power systems. Considering the highly varying and complex features of PVPF, this paper constitutes a novel hybrid deep learning forecasting method. A similar day selection method based on the levy-flight beetle antennae search algorithm is proposed to select historical days similar to the forecast day from the real-time massive data. The Pearson correlation coefficient method is utilized to select the main meteorological factors while wavelet packet decomposition is used to decompose and reconstruct the original PV power into a series of sub-signals. A deep learning model taking the sub-signals of similar days as network inputs is established with a group of gated recurrent units (GRU), where the hyperparameters of each GRU network are effectively optimized. The forecasting result of each sub-signal is integrated to obtain the final forecasting PV power value. The simulation regarding the 5-min ahead PVPF is carried out based on a real-world dataset from Alice Spring, Australia. The simulation comparisons indicate that the proposed hybrid deep learning method outperforms other competitive PVPF methods in terms of both forecasting accuracy and computational efficiency.

Keywords: Photovoltaic power; Forecasting; Deep neural network; Similar day analysis; Wavelet packet decomposition (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:271:y:2023:i:c:s0360544223003572

DOI: 10.1016/j.energy.2023.126963

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