PHOTOVOLTAIC POWER PREDICTION MODEL BASED ON EMPIRICAL MODE DECOMPOSITION AND TRANSFORMER-TCN WITH FEATURE ENHANCEMENT

Zhang Haiqing; Liu Hang; Tang Xin; Yang Mengji; Yu Xi; Li Daiwei

doi:10.19912/j.0254-0096.tynxb.2025-0685

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (9) : 203-211. DOI: 10.19912/j.0254-0096.tynxb.2025-0685

PHOTOVOLTAIC POWER PREDICTION MODEL BASED ON EMPIRICAL MODE DECOMPOSITION AND TRANSFORMER-TCN WITH FEATURE ENHANCEMENT

Zhang Haiqing^1,2, Liu Hang¹, Tang Xin¹, Yang Mengji^3,4, Yu Xi³, Li Daiwei^1,2

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Abstract

Photovoltaic （PV） power generation, as a crucial source of clean energy, requires accurate power forecasting to facilitate efficient power dispatch. PV power is highly influenced by meteorological factors and exhibits significant intermittency and volatility. Traditional time series forecasting models often fail to capture the dynamic correlations among meteorological factors, leading to limited forecasting accuracy. Meanwhile, single deep learning architectures struggle to effectively capture the intricate interplay between multi-scale temporal dependencies and localized feature variations. To address these challenges, this paper proposes a dual-channel hybrid forecasting model integrating empirical mode decomposition （EMD）, Transformer, and temporal convolutional network （TCN）. EMD is employed to decompose the raw PV power signal into a set of intrinsic mode functions （IMFs）. The stationarity of each IMF component is quantified using sample entropy （SE）, guiding the selection of tailored predictive models for components with distinct stationarity characteristics. An enhanced residual transformer （Enresformer）, which incorporates residual networks, and a gated network-enhanced TCN model are employed to forecast these components, with subsequent ensembling of their outputs. An error correction mechanism is introduced to further refine prediction accuracy. Comparative experiments based on multiple real-world PV datasets show that the proposed model significantly outperforms existing deep learning and hybrid models in terms of forecasting accuracy, providing an efficient and reliable solution for PV power prediction in practical power stations.

Key words

photovoltaic power / forecasting / empirical mode decomposition / transformer / residual network / temporal convolutional network

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Zhang Haiqing, Liu Hang, Tang Xin, Yang Mengji, Yu Xi, Li Daiwei. PHOTOVOLTAIC POWER PREDICTION MODEL BASED ON EMPIRICAL MODE DECOMPOSITION AND TRANSFORMER-TCN WITH FEATURE ENHANCEMENT[J]. Acta Energiae Solaris Sinica. 2026, 47(9): 203-211 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0685

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