Cooling, Heat, Electricity and Gas Joint Load Forecasting Method Based on Modal Decomposition and Dynamic Model Selection

Accurate joint forecasting of electricity, cooling, heating, and gas loads is essential to the coordinated operation of integrated energy systems. However, multivariate energy load sequences exhibit strong cross-carrier coupling, non-stationarity, and heterogeneous fluctuation characteristics, which limits the performance of conventional independent forecasting and fixed-model approaches. To address these challenges, this study proposes a joint load forecasting framework that integrates tabular Q-learning-assisted multivariate variational mode decomposition, sample-entropy-based reconstruction, and dynamic model selection. First, tabular Q-learning is employed to select the MVMD penalty factor and the four load sequences are synchronously decomposed to preserve the coupling relationships among components with common center frequencies. Second, sample entropy is used to reconstruct the decomposed modes into high-frequency, low-frequency, and residual subsequences, thereby reducing forecasting complexity while retaining relevant temporal features. Third, a dynamic model selection mechanism evaluates SVR, BiLSTM, XGBoost, and LightGBM and assigns an appropriate predictor to each reconstructed subsequence according to its forecasting performance. The framework is evaluated using daily electricity, cooling, heating, and gas load data collected from the Tempe Campus of Arizona State University from 2016 to 2020. A rolling input window of 56 days is used to forecast the subsequent seven days. Compared with the benchmark methods, the proposed framework achieved the best overall composite performance and competitive forecasting accuracy across the four load types. These results provide a potentially useful forecasting basis for operational decision-making in integrated energy systems.