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OA北大核心CSTPCD
Transformer Based Multi Load Short-Term Forecasting of Integrated Energy System Considering Integrated Demand Response
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…查看全部>>
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 differ…查看全部>>
李云松;张智晟
青岛大学电气工程学院 青岛 266071青岛大学电气工程学院 青岛 266071
动力与电气工程
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
Integrated energy systemintegrated demand responsecoupling responsegraph neural networkTransformer modelmulti load short-term forecasting
《电工技术学报》 2024 (19)
6119-6128,10
国家自然科学基金资助项目(52077108).
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