Short-Term Load Forecasting Integrating Multi-Temporal Segmenting and Mamba

Abstract

Accurate residential user cluster load forecasting is essential for advancing demand-side management in smart distribution grids and assisting power grid companies in peak–valley regulation. To overcome the limitations of traditional load forecasting methods, this study combines multi-temporal segmentation with the Mamba (TS-Mamba) architecture to develop a high-precision forecasting framework. The historical load sequence is first segmented into several temporal segments. A convolution- based perceptron subsequently processes these segments to capture local features, while temporal dependencies are modeled through a selective state-space formulation. A gated perceptron structure is further integrated to improve feature interaction and model robustness. In addition, residual connections inspired by ResNet are introduced to stabilize network training. Experimental results across different load datasets show that the proposed method consistently outperforms conventional neural network and Transformer-based forecasting models. Moreover, the parameter-efficient state-space structure, combined with temporal segmentation and perceptron- based feature learning, enables the model to maintain stable prediction performance even in small-sample scenarios. The proposed model relies on historical load patterns for learning temporal dependencies. Therefore, its performance may degrade when the load series contains abrupt structural changes, extreme events, or highly irregular patterns that are not sufficiently represented in the training data. In addition, the segmentation strategy introduces hyperparameters that may require careful tuning for different datasets. The proposed model effectively improves the accuracy of short-term load forecasting. It is worth noting that in the small-sample scenario, TS-Mamba can maintain reliable predictive performance while reducing the number of model parameters.