TAN: A Temporal Attention Network for Photovoltaic Power Forecasting

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Chinese explanation / 中文解读

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Original abstract

The increasing penetration of distributed photovoltaic (PV) systems poses significant challenges to power grid stability, making accurate medium and long-term PV power forecasting essential.This paper proposes a Temporal Attention Network (TAN) for multi-step PV power forecasting.TAN adopts a pure temporal self-attention architecture with a learnable temporal embedding module, enabling effective modeling of periodic and non-stationary temporal dependencies without relying on recurrent or convolutional structures.The proposed model is evaluated on real-world PV data under 24-hour and 48-hour forecasting horizons.Performance is assessed using multiple metrics, including RMSE, MAE, MAPE, R 2 , and confidence intervals derived from rolling forecasting evaluation.Experimental results show that TAN achieves consistently lower forecasting errors and higher explanatory power than representative recurrent, convolutional, hybrid, and Transformer-based baselines in both forecasting settings.Moreover, TAN maintains competitive performance with moderate model complexity.These results indicate that TAN provides an effective and scalable attention-based solution for medium-and long-term PV power forecasting in renewable energy -integrated power systems.

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