SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON SIMILAR DAY CLUSTERING AND WOA-VMD-TCN-TRANSFORMER MODEL

Zhao Danyang; Tang Xujing; Wang Tian; Guo Wei

doi:10.19912/j.0254-0096.tynxb.2024-1187

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (11) : 210-218. DOI: 10.19912/j.0254-0096.tynxb.2024-1187

SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON SIMILAR DAY CLUSTERING AND WOA-VMD-TCN-TRANSFORMER MODEL

Zhao Danyang¹, Tang Xujing^1-2, Wang Tian¹, Guo Wei¹

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Abstract

To solve the problem that PV power fluctuates significantly and is difficult to predict, this paper proposes a combined PV power prediction model based on similar day clustering and an WOA-WMD-TCN-Transformer model. Firstly, K-means ++ is used to cluster similar days. Then WOA was used to optimize VMD parameters, and the PV power sequence was decomposed into multiple Intrinsic Mode functions（IMFs）. The IMF components and meteorological factors were weighted and combined into a new feature vector and fed into the subsequent model. Based on TCN-Transformer, IMF under different weather conditions can be predicted separately and the predicted value can be obtained after superposition. Finally, the photovoltaic power generation and meteorological data of Hanwha Solar Photovoltaic Station, a desert solar Research Center in Alice Springs, Central Australia, were used as an example to verify the validity of the model. Ablation experiments and comprehensive evaluation show that the proposed model can achieve high prediction accuracy under various weather conditions.

Key words

forecasting / deep learning / variational mode decomposition / similar day clustering / TCN-Transformer / photovoltaic

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Zhao Danyang, Tang Xujing, Wang Tian, Guo Wei. SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON SIMILAR DAY CLUSTERING AND WOA-VMD-TCN-TRANSFORMER MODEL[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 210-218 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1187

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