<![CDATA[Residential load forecasting is essential for maintaining grid stability and energy management in smart grids. However, achieving accurate real-time forecasting under resource constraints remains challenging because Transformer and LSTM models can be computationally demanding, while lightweight linear models such as DLinear have limited modeling flexibility. This study investigates whether a hierarchical separable convolutional framework can provide accurate and efficient residential load forecasting. To address this, SeparableCLF, a lightweight hierarchical decomposition model using depthwise separable convolution, is proposed and evaluated on hourly OpenEI residential load data from 20 U.S. states (2012) at forecast horizons of 6, 12, 24, 48, and 96 h. Relative to DLinear, SeparableCLF reduced MAPE by 0.93, 1.54, and 0.79 percentage points at 24, 48, and 96 h, respectively while requiring substantially fewer parameters than Transformer and LSTM models.SeparableCLF achieved the lowest MAPE at 12, 24, and 48 h. DLinear achieved the lowest errors at 6 h, whereas LSTM achieved the lowest MAPE at 96 h; at 96 h, SeparableCLF retained the lowest MAE, MSE, and RMSE among the compared models, indicating suitability for real-time forecasting on smart meters and edge-based smart grid devices.]]>
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