基于辅助序列耦合和多任务学习的综合能源系统多能源负荷预测模型

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Honghan Zhao, Yuchen Wu
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引用次数: 0

摘要

高精度的多能源负荷预测(MELF)对于综合能源系统(IES)的经济运行和优化调度至关重要。在此类系统中,电力、热力和冷负荷可能呈现出复杂和高度耦合的关系。利用多能源负荷的耦合关系可以提高 MELF 的精度。为解决这一问题,作者提出了一种名为 CAFormer(耦合辅助变压器)的新型框架,利用耦合辅助预测和多任务学习(MTL)来改进 IES 中的 MELF。CAFormer 采用辅助预测耦合策略。其目的是通过将不同序列投影到同一耦合空间来构建耦合辅助序列,以捕捉它们之间的相互依赖关系。加入耦合空间后,模型空间结构更加复杂。此外,作者的框架偏离了将多个序列的预测任务视为多个任务的传统方法,将对比学习任务和预测任务整合为一个单一的多任务范式。这就避免了在传统的多能负荷多任务预测中,当不同负荷直接共享信息时产生的序列纠缠问题。基于耦合空间相似性的 MTL 有助于学习序列之间的耦合关系,同时保持原始序列特征并完善耦合辅助序列的生成。实验结果表明,作者提出的模型可以深入探索能源系统之间的耦合关系,与最先进的模型相比,预测精度更高,预测适用性更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multienergy load forecasting model for integrated energy systems based on coupling auxiliary sequences and multitask learning

Multienergy load forecasting model for integrated energy systems based on coupling auxiliary sequences and multitask learning

Multienergy load forecasting (MELF) with high accuracy is crucial for the economic operation and optimal dispatch of the integrated energy system (IES). Within such systems, electrical, heat, and cold loads may exhibit complex and highly coupled relationships. The accuracy of MELF can be improved by exploiting the coupling of multienergy loads. To address this issue, the authors propose a novel framework named CAFormer (coupling auxiliary transformer) that leverages coupling auxiliary forecasting and multitask learning (MTL) to improve MELF in IES. CAFormer adopts an auxiliary forecasting coupling strategy. The aim is to construct coupling auxiliary sequences by projecting different sequences onto the same coupling space to capture the interdependencies between them. The model space structure is more complex when incorporating the coupling space. Furthermore, the authors’ framework deviates from the traditional approach of treating the prediction task of multiple sequences as multiple tasks by integrating the contrastive learning task and the prediction task into a single multitask paradigm. This avoids the issue of sequence entanglement that arises when different loads directly share information in the traditional multitask prediction of multiple energy loads. MTL based on coupling space similarity helps to learn the coupling relationships between sequences while maintaining the original sequence characteristics and refining the generation of coupling auxiliary sequences. The experimental results demonstrate that the authors’ proposed model can thoroughly explore the coupling relationships among energy systems and exhibit higher prediction accuracy and better prediction applicability than those of state-of-the-art models.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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