Day-Ahead Cooling Load Forecasting for District Cooling System Based on Baseline-Morphology Decomposition

Abstract

Against the backdrop of global climate change and energy structure transition, district energy systems have garnered significant attention for their efficiency and sustainability. Accurate load forecasting is crucial for enhancing the operational efficiency of district cooling systems. However, as typical dynamic time-varying systems, building complexes within district energy stations exhibit load characteristics influenced by multi-scale features. Furthermore, traditional load forecasting models employ single-scale analysis without specifically modeling these multi-scale characteristics, resulting in insufficient generalization capabilities of data-driven models under dynamic, time-varying scenarios. This paper proposes a multi-step forecasting model structure based on baseline-morphology decomposition to address the coupling of multi-scale characteristics. By decomposing load into baseline and morphological components, separate prediction models—a backpropagation neural network (BP) and a K-means clustering-decision tree (DT) classification prediction model—are constructed, overcoming the challenge of capturing multi-scale features in traditional methods. The results show that, during the four-month test period from August to November 2024, the proposed model achieves MAPE values ranging from 8.91% to 12.57% under the peak and transitional cooling conditions represented in the dataset. Compared to direct structure, recursive structure, and multi-input multi-output (MIMO) structure, it reduces errors by 1.49% to 19.62% while achieving remarkable advantages in training efficiency.