Knowledge-Data Fusion Model for Multivariate Load Short-Term Forecasting of Integrated Energy System

doi:10.1007/s12204-024-2740-1

Abstract

Abstract: The short-term forecasting of multiple loads is crucial for the optimization and scheduling of integrated energy system (IES). However, the load within the IES exhibits diversified and strongly coupled characteristics, which seriously affects the forecast accuracy. Moreover, only using deep learning forecasting methods cannot analyze the factors that affect the forecast results, which is not conducive to guiding the optimization and scheduling of comprehensive energy systems. Therefore, a multivariate load forecasting model based on knowledge-guided multi-task spatial-temporal synchronous graph convolutional network is proposed. Firstly, the user clusters are classified according to the energy-using characteristics of different buildings. Then, the domain knowledge base is built by combining the dimensionless trends of different groups and expert experience. At the same time, the input features are filtered based on the improved maximum information coefficient method to construct spatialtemporal graph data, forming a more refined and efficient input sample data. Finally, the knowledge-data fusion model for multivariate load forecasting is constructed to predict local fluctuations of the multivariate load series and reconstruct the load ratio. The IES data set of Arizona State University Tempe Campus is taken as a test case. The results show that the proposed method is interpretable, has higher forecast accuracy and has better generalization ability.

Key words: integrated energy system (IES), multivariate load forecasting, knowledge guide, multi-task learning, spatial-temporal synchronous graph convolutional network

摘要： 多元负荷的短期预测对于综合能源系统（IES）的优化调度至关重要。但是，综合能源系统负荷呈现多元化、强耦合特征，严重影响了预测精度。以往仅仅使用深度学习预测方法无法对预测结果的影响因素进行解释，不利于指导综合能源系统的优化调度。为此，提出了一种基于知识引导的多任务时空同步图卷积网络的多元负荷预测模型。首先，根据不同建筑的能源使用特征对用户集群进行分类。然后，结合不同群体的无量纲趋势和专家经验，建立领域知识库。同时，基于改进的最大信息系数法对输入特征进行筛选，构建时空图数据，形成更为精细、高效的输入样本数据。最后，构建了多变量负荷预测的知识-数据融合模型，用于预测多变量负荷序列的局部波动，重构负荷比，负荷比与前一时刻数据相乘即可得到多元负荷的预测结果。以亚利桑那州立大学坦佩校区IES数据集作为测试案例。结果表明，该方法具有可解释性、较高的预测精度和较好的泛化能力。

关键词: 综合能源系统，多元负荷预测，知识引导，多任务学习，时空同步图卷积网络

CLC Number:

TM714

Wu Lizhen, Zhao Yifan, Qin Wenbin, Chen Wei. Knowledge-Data Fusion Model for Multivariate Load Short-Term Forecasting of Integrated Energy System[J]. J Shanghai Jiaotong Univ Sci, 2026, 31(2): 499-514.

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