Flexible Load Forecasting Method Based on Feature Decomposition and Adaptive Response Mechanism

Abstract

With the ongoing reform of electricity markets and the increasing penetration of demand response (DR) resources, load exhibits stronger price-related uncertainty, posing significant challenges to short-term load forecasting. To address this issue, this paper proposes a DR-oriented forecasting framework that integrates feature decomposition, adaptive response modeling, and deep learning techniques. First, electricity price signals and load data are processed to identify DR-related features and extract representative response characteristics. An adaptive response mechanism (ARM) is then developed to learn time-varying statistical response characteristics from historical load–price states and to construct an explicit price-related correction for the baseline load forecast, while a hybrid CNN-BiLSTM forecasting model is constructed to capture both local spatiotemporal patterns and long-term temporal dependencies. In addition, an optimal weighting strategy is adopted to improve forecasting performance. Case studies demonstrate that the proposed method consistently outperforms conventional forecasting models that neglect DR effects, yielding notable reductions in MAE, RMSE, and MAPE, together with an increase in the coefficient of determination (R2). By effectively quantifying the dynamic price–load interaction, the proposed framework improves forecasting accuracy, robustness, and interpretability, providing a practical solution for short-term load forecasting in electricity markets with DR participation.