数据驱动的综合能源系统负荷预测综述

Review of Data-driven Load Forecasting for Integrated Energy System

为了进一步提高能源的利用效率,综合能源系统(integrated energy system,IES)使各种能源系统间联系更加紧密,已经成为能源转型过程中一种重要的能源利用方式。IES负荷预测作为IES需求侧能量预测的重要方面,已经成为IES规划与运行调度的首要前提,要求有效地学习多类能源耦合信息,并准确地对多元化用能需求进行预测。首先,对IES用能特性进行分析;然后,对IES负荷预测研究现状进行综述,着重介绍了数据驱动技术在IES负荷预测问题中的应用状况;最后,对IES需求侧能量预测体系发展方向进行展望,并设想构建涵盖多能源耦合、多时间尺度、多空间层级和多管理环节的IES多维度协同预测体系。

To further improve energy efficiency,integrated energy system(IES)has realized closer interactions of various energy vectors and has become an important energy utilization way.IES load forecasting has become the primary prerequisite for IES planning and operation scheduling.It requires effective learning of multiple energy coupling information and accurate forecasting of multiple loads.This paper firstly analyzed energy consumption or demand characteristics in IES.This paper then provided a comprehensive overview on current technical works of IES load forecasting and particularly introduced the existing applications of data-driven learning techniques in this domain.Finally,the paper prospected future development of demand-side energy forecasting and envisaged constructing collaborative and multi-dimensional energy forecasting frameworks or systems in IES,taking advantages of the novel changes of multiple energy interactions of different energy vectors,multiple temporal horizons of load forecasts,multiple spatial aggregation levels of the energy systems,and multiple side combinations around energy.