{"title":"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","authors":"Honghan Zhao, Yuchen Wu","doi":"10.1049/gtd2.13023","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.13023","citationCount":"0","resultStr":"{\"title\":\"Multienergy load forecasting model for integrated energy systems based on coupling auxiliary sequences and multitask learning\",\"authors\":\"Honghan Zhao, Yuchen Wu\",\"doi\":\"10.1049/gtd2.13023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.13023\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/gtd2.13023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/gtd2.13023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
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.