<ul><li>Residential electricity demand forecasting is important for efficient energy management and grid stability.</li><li>A novel deep learning framework called SPDNet is proposed to tackle the challenges of capturing intricate temporal dynamics in electricity demand data.</li><li>SPDNet consists of two main modules: Seasonal-Trend Decomposition Module (STDM) and Periodical Decomposition Module (PDM).</li><li>Extensive experiments show that SPDNet outperforms traditional and advanced models in both accuracy and efficiency.</li></ul>

SPDNet: Seasonal-Periodic Decomposition Network for Advanced Residential Demand Forecasting

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