Short-Term Load Forecasting in Price-Volatile Markets: A Pattern-Clustering and Adaptive Modeling Approach

Under the ongoing electricity market reforms, short-term load forecasting (STLF) is increasingly challenged by pronounced non-stationarity driven by price fluctuations. This study proposes an adaptive STLF framework tailored to price-induced non-stationarity. Firstly, a market state identification method based on load–price joint clustering is developed to structurally model the temporal interactions between price and load. It allows the automatic extraction of typical market patterns and helps uncover how price fluctuations drive load variations. Secondly, a gated mixture forecasting network is proposed to dynamically adapt to the inertia of historical price fluctuations. By integrating parallel branches with an adaptive weighting mechanism, the model dynamically captures historical price features and achieves both rapid response and steady correction under market volatility. Finally, a Transformer-based expert model with multi-scale dependency learning is introduced to capture sequential dependencies and state transitions across different load regimes through self-attention, thereby enhancing model generalization and stability. Case studies using real market data confirm that the proposed approach delivers substantial performance improvements, offering reliable support for system dispatch and market operations. Relative to mainstream baseline models, it reduces MAPE by 1.08–2.62 percentage points.