SHORT-TERM PV POWER FORECASTING BASED ON CLUSTERING SABO-VMD AND ENSEMBLE NEURAL NETWORKS

Feng Jianming; Xiwang&#x000b7;Abuduwayiti; Lin Hong

doi:10.19912/j.0254-0096.tynxb.2023-1681

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (2) : 357-366. DOI: 10.19912/j.0254-0096.tynxb.2023-1681

SHORT-TERM PV POWER FORECASTING BASED ON CLUSTERING SABO-VMD AND ENSEMBLE NEURAL NETWORKS

Feng Jianming, Xiwang·Abuduwayiti, Lin Hong

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Abstract

To address the challenges of low prediction accuracy when using a single model for photovoltaic power generation forecasting under varying weather conditions, a composite neural network model is established based on a convolutional neural network and a bidirectional long short-term memory network. The osprey optimization algorithm is introduced to optimize the parameters of ensemble neural networks. Additionally, an attention mechanism is incorporated to emphasize the influence of strong correlation factors. We employ Gaussian mixture model clustering to categorize historical photovoltaic data into various weather types and propose a subtraction average-based optimizer algorithm to optimize variational mode decomposition for data decomposition based on different weather types. Experimental results demonstrate that optimizing data decomposition parameters using SABO-VMD effectively enhances prediction accuracy. In comparison to other combined prediction models,through experimental comparisons, the accuracy of the model proposed in this paper is significantly higher.

Key words

photovoltaic power / variational mode decomposition / neural networks / power forecasting / attention mechanism / Gaussian mixture model clustering

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Feng Jianming, Xiwang·Abuduwayiti, Lin Hong. SHORT-TERM PV POWER FORECASTING BASED ON CLUSTERING SABO-VMD AND ENSEMBLE NEURAL NETWORKS[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 357-366 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1681

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