SHORT-TERM PHOTOVOLTAIC POWER PREDICTION MODEL USING MULTI-SCALE PERIODIC FEATURES

Chen Wanzhi; Liu Haoyang; Wang Tianyuan

doi:10.19912/j.0254-0096.tynxb.2025-0766

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (9) : 327-340. DOI: 10.19912/j.0254-0096.tynxb.2025-0766

SHORT-TERM PHOTOVOLTAIC POWER PREDICTION MODEL USING MULTI-SCALE PERIODIC FEATURES

Chen Wanzhi¹, Liu Haoyang¹, Wang Tianyuan²

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Abstract

In response to the problem of limitations in modeling nonlinearity, volatility, and multi-scale periodicity characteristics in photovoltaic power prediction, a short-term photovoltaic power prediction model using multi-scale periodic features was proposed. Firstly, the raw data are preprocessed and the Pearson correlation coefficient features are reconstructed, then cross-feature interactions are optimized through a gated aggregation redistribution mechanism. Secondly, a dual-phase periodicity-residual optimization mechanism is established, where residual components obtained through residual cycle forecasting are processed by Transformer encoders, while periodic components undergo multi-scale periodic feature fusion. The periodic processing module employs temporally adaptive multi-scale convolutional kernels and a time-dimension averaging fusion strategy to enhance periodic pattern recognition. Finally, predictions are generated through integrated outputs from both residual and periodic modeling paths. Experimental evaluations across three real datasets show that the proposed model outperforms comparison models across various horizons. Specifically, in PV Dataset 1, the average MAE and RMSE are reduced by 4.10% and 3.98% respectively compared to the second-best model; in PV Dataset 2, these indicators are reduced by 3.31% and 1.51%; in the short-term tasks of the ETTh1 dataset, the MAE is reduced by 1.66%. Additionally, the model achieves the highest R² in multiple tasks, verifying its superior predictive performance and generalization ability in short-term photovoltaic power prediction.

Key words

photovoltaic power / short-term prediction / time series / Transformer / neural networks / multi-scale periodic features

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Chen Wanzhi, Liu Haoyang, Wang Tianyuan. SHORT-TERM PHOTOVOLTAIC POWER PREDICTION MODEL USING MULTI-SCALE PERIODIC FEATURES[J]. Acta Energiae Solaris Sinica. 2026, 47(9): 327-340 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0766

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