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Separable Convolutional Hierarchical Decomposition for Lightweight Residential Load Forecasting in Smart Grids
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 foreca...
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Multi-Horizon Short-Term Residential Load Forecasting Using Decomposition-Based Linear Neural Network
Advance Sustainable Science, Engineering and Technology (ASSET)
Vol 7
, No 3
(2025)
Short-Term Load Forecasting is crucial for grid stability and real-time energy management, particularly in residential settings where consumption is highly volatile and influenced by behavioral and external factors. Traditional models struggle to capture complex, non-linear patterns. This study proposes a forecasting framework based on the DLinear model, which decomposes time series data into trend and seasonal components using a simple linear neural network architecture. Designed for multi-hori...
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