A novel trend and periodic characteristics enhanced decoupling framework for multi-energy load prediction of regional integrated energy systems

Efficient and accurate multi-energy load forecasting is crucial for achieving supply-demand balance in regional integrated energy systems(RIES). Existing research primarily focuses on modeling the coupling between different loads, with limited consideration of modeling from the perspective of sequential temporal information. Excessive emphasis on load coupling can lead to reduced accuracy in long-term load forecasting for RIES. To address this issue, this paper proposes a trend and periodicity feature-enhanced decoupling prediction framework to fully capture the long-term dependency relationship within sequences. Firstly, load data usually has significant trends and seasonal variations, and by decomposing load data into trend and seasonal features, it is possible to forecast for different patterns of change. Next, a decoupling prediction framework is proposed that adequately considers sequential temporal information, trend information, and seasonal information. The proposed framework enables accurate predictions for electricity load, cooling load, and heating load. Finally, comprehensive and extensive validation of the proposed prediction method is conducted using actual data from RIES. The comparative results demonstrate that the method proposed in this paper outperforms other Transformer-based model algorithms, with average improvements of 20.8%, 24.5%, and 21.7% in the evaluation metrics for electricity load, cooling load, and heating load forecasting, respectively, indicating superior predictive accuracy and wider applicability.