Open Access

ARTICLE

Ultrashort-Term Power Prediction of Distributed Photovoltaic Based on Variational Mode Decomposition and Channel Attention Mechanism

Zhebin Sun¹, Wei Wang¹, Mingxuan Du², Tao Liang¹, Yang Liu¹, Hailong Fan³, Cuiping Li², Xingxu Zhu², Junhui Li^2,*

1 Intelligent Distribution Network Department, Inner Mongolia Electric Power Economics and Technology Research Institute, Hohhot, 010020, China
2 Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin, 132012, China
3 Production Technology Department, Inner Mongolia Power (Group) Co. Ltd., Hohhot, 010010, China

* Corresponding Author: Junhui Li. Email:

Energy Engineering 2025, 122(6), 2155-2175. https://doi.org/10.32604/ee.2025.062218

Received 12 December 2024; Accepted 10 March 2025; Issue published 29 May 2025

Abstract

Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation. This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition (VMD) and Channel Attention Mechanism. First, Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power. Second, the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition (VMD). Finally, the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM model. In this model, the convolutional neural network (CNN) and channel attention mechanism dynamically adjust the weights while capturing the spatial features of the input data to improve the discriminative ability of key features. The extracted data is then fed into the bidirectional long short-term memory network (BiLSTM) to capture the time-series features, and the final output is the prediction result. The verification is conducted using a dataset from a distributed photovoltaic power station in the Northwest region of China. The results show that compared with other prediction methods, the method proposed in this paper has a higher prediction accuracy, which helps to improve the proportion of distributed PV access to the grid, and can guarantee the safe and stable operation of the power grid.

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Keywords

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