考虑综合需求响应的Transformer-图神经网络综合能源系统多元负荷短期预测  

Transformer Based Multi Load Short-Term Forecasting of Integrated Energy System Considering Integrated Demand Response

作　　者：李云松 张智晟 Li Yunsong;Zhang Zhisheng(College of Electrical Engineering,Qingdao University,Qingdao266071,China)

机构地区：[1]青岛大学电气工程学院,青岛266071

出　　处：《电工技术学报》2024年第19期6119-6128,共10页Transactions of China Electrotechnical Society

基　　金：国家自然科学基金资助项目(52077108)。

摘　　要：为提高在需求响应情境下,综合能源系统的多元负荷短期预测精度,基于消费者心理学、响应不确定性原理、耦合响应原理,构建了考虑综合需求响应的Transformer-图神经网络(Trans-GNN)预测模型。通过响应不确定性随电价差产生的变化规律和消费者心理学原理,量化在不同概率条件下的电力需求响应结果。通过耦合响应原理,求解包含冷、热耦合响应的综合需求响应信号,最终利用注意力机制将综合需求响应信号引入Trans-GNN预测模型,提高网络模型在需求响应情境下的多元负荷预测能力。算例分析结果表明,该模型能有效地提高预测精度,为计及综合需求响应的多元负荷预测研究提供了一定的理论基础。The accurate forecasting of multi load in an integrated energy system is imperative for ensuring the efficient and secure operation of diverse energy sources.Demand response technology not only enhances the equilibrium between energy supply and demand but also induces changes in users'general energy consumption habits,thereby amplifying the complexity and uncertainty of load forecasting.While existing research explores the coupling relationships among different energy sources in integrated energy systems and employs artificial intelligence methods for predictions,there is a noticeable gap in research concerning multi load forecasting that incorporates integrated demand response.To address these issues,this paper presents a Trans-GNN prediction model that incorporates integrated demand response considerations.Through the mathematical modeling of integrated demand response signals and their incorporation as input variables into the deep learning model,the accuracy of load predictions in demand response scenarios is improved.Firstly,adhering to consumer psychology principles,we establish the power demand response center curve.By statistically calculating the fluctuation value of power demand response under various probability conditions,considering the correlation between response uncertainty and electricity price difference,we derive the power demand response signal with due consideration to uncertainty.Employing this signal and the coupling response principle,we ascertain the demand coupling response signal for cold and heat loads,culminating in the derivation of a integrated demand response signal.Then the Trans-GNN model integrates this signal,historical energy consumption data,and meteorological data for prediction.Through Transformer's attention mechanism,the model realizes the probabilistic understanding of the integrated demand response signal,and dynamically extracts and filters the historical energy consumption data.Finally,the graph neural network is used to complete the further analysis of the input data

关 键 词：综合能源系统 综合需求响应 耦合响应 图神经网络 Transformer模型 多元负荷短期预测 

分 类 号：TM715[电气工程—电力系统及自动化]