基于耦合特征与多任务学习的综合能源系统短期负荷预测

Short-term Load Forecasting for Integrated Energy System Based on Coupling Features and Multi-task Learning

在区域型综合能源系统(IES)内各负荷间耦合程度逐渐增强和对更准确、可靠的用能预测需求日益提高的背景下,提出一种基于耦合特征构造及多任务学习的IES冷热电负荷短期预测方法。首先,从特征工程的角度利用耦合特征挖掘算法构造IES冷热电负荷耦合特征变量,提取不同能源负荷需求间的耦合特征,进而将负荷历史数据、耦合特征变量及气温等外生变量作为模型输入,利用多任务学习的共享机制建立IES的负荷预测模型,使得各能源预测子任务间的高维特征及模型参数能够通过基于长短期记忆神经网络搭建的共享学习层相互借鉴,以实现对负荷间耦合特征的充分挖掘和利用。以美国亚利桑那州立大学坦佩校区IES为例,通过预测结果精度对比和深度学习模型可解释性研究,证明所提出的预测方法可以有效提高区域型IES冷热电短期负荷预测的精度。

In the background of increasing coupling degree among different loads in the regional integrated energy system(IES)and increasing demand for more accurate and reliable energy consumption forecasting,a short-term forecasting method of cooling,heating and electrical loads in IES based on coupling feature construction and multi-task learning is proposed.First,from the perspective of feature engineering,the coupling feature mining algorithm is used to construct the coupling feature variables of cooling,heating and electrical loads in IES,and the coupling features among different energy load demands are extracted.Then,taking the exogenous variables such as load history data,coupling feature variables and the temperature as the model inputs,the load forecasting model of IES is established by using the sharing mechanism of multi-task learning,which makes the highdimensional features and model parameters among energy forecasting subtasks be used for mutual reference through a shared learning layer built by long short-term memory neural network,so that the full exploitation and utilization of load coupling features can be realized.Taking the IES of Tempe Campus of Arizona State University as an example,this paper proves that the proposed forecasting method can effectively improve the short-term forecasting accuracy of cooling,heating and electrical loads in the regional IES,through the accuracy comparison of forecasting results and the interpretability research of the deep learning model.