IEEE Transactions on Sustainable Energy最新文献_第3页

A Photovoltaic-Grid Integrated System for the Residential Power Management 用于住宅电力管理的光伏电网集成系统

IF 8.8 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-09-03 DOI: 10.1109/tste.2024.3454060

Ande Bala Naga Lingaiah, Narsa Reddy Tummuru

引用次数: 0

Physics-Informed Reinforcement Learning for Real-Time Optimal Power Flow with Renewable Energy Resources 利用可再生能源实时优化电力流的物理信息强化学习

IF 8.8 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-08-30 DOI: 10.1109/tste.2024.3452489

Zhuorui Wu, Meng Zhang, Song Gao, Zheng-Guang Wu, Xiaohong Guan

引用次数: 0

Exploiting the Flexibility of District Heating System for Distribution System Operation: Set-Based Characterization and Temporal Decomposition 利用区域供热系统的灵活性促进配电系统的运行：基于集合的特征描述和时间分解

IF 8.8 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-08-30 DOI: 10.1109/tste.2024.3452560

Weitao Chen, Xiaojun Wang, Wei Wei, Yin Xu, Jianzhong Wu

引用次数: 0

Scheduling Multiple Industrial Electrolyzers in Renewable P2H Systems: A Coordinated Active-Reactive Power Management Method 可再生 P2H 系统中多个工业电解槽的调度：有功-无功功率协调管理方法

IF 8.8 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-08-28 DOI: 10.1109/tste.2024.3450503

Yangjun Zeng, Yiwei Qiu, Jie Zhu, Shi Chen, Buxiang Zhou, Jiarong Li, Bosen Yang, Jin Lin

引用次数: 0

Modeling and Optimization Operation of Improved Power-to-Hydrogen-and-Heat method at Low Temperature for Reducing Carbon Emissions 用于减少碳排放的改进型低温动力制氢和供热方法的建模与优化运行

IF 8.8 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-08-28 DOI: 10.1109/tste.2024.3448366

Haohui Ding, Qinran Hu, Tao Qian, Zaijun Wu

引用次数: 0

Optimal Dispatch Strategy for a Multi-microgrid Cooperative Alliance Using a Two-Stage Pricing Mechanism 使用两阶段定价机制的多微网合作联盟优化调度策略

IF 8.8 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-08-26 DOI: 10.1109/tste.2024.3449909

Yonghui Nie, Zhi Li, Jie Zhang, Lei Gao, Yang Li, Hengyu Zhou

引用次数: 0

Graph-based Large Scale Probabilistic PV Power Forecasting Insensitive to Space-Time Missing Data 对时空缺失数据不敏感的基于图的大规模概率光伏功率预测

IF 8.8 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-08-21 DOI: 10.1109/tste.2024.3447023

Keunju Song, Minsoo Kim, Hongseok Kim

引用次数: 0

Shape Optimization of a Point Absorber Wave Energy Converter for Reduced Current Drag and Improved Wave Energy Capture Using Neural Networks and Genetic Algorithms 利用神经网络和遗传算法优化自反应点吸收器波能转换器的形状，以减少电流阻力并提高波能捕获率

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-08-16 DOI: 10.1109/TSTE.2024.3443117

Weihan Lin;Xiaofan Li;Lei Zuo

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引用次数: 0

An Adaptive Transfer Learning Framework for Data-Scarce HVAC Power Consumption Forecasting 用于数据保密暖通空调耗电量预测的自适应迁移学习框架

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-08-16 DOI: 10.1109/TSTE.2024.3444689

Yanan Zhang;Gan Zhou;Zhan Liu;Li Huang;Yucheng Ren

{"title":"An Adaptive Transfer Learning Framework for Data-Scarce HVAC Power Consumption Forecasting","authors":"Yanan Zhang;Gan Zhou;Zhan Liu;Li Huang;Yucheng Ren","doi":"10.1109/TSTE.2024.3444689","DOIUrl":"10.1109/TSTE.2024.3444689","url":null,"abstract":"Heating, ventilation, and air conditioning (HVAC) systems constitute a large proportion of building energy consumption and provide considerable potential for power grid regulation. While the HVAC power consumption forecasting task is generally straightforward with sufficient historical data, it becomes challenging when dealing with scarce data. Such situation is common in cases of intermittent data collection or early system implementations, where precise forecasting is required despite limited data available. Considering accessible datasets from nearby or similar HVAC systems through energy management systems, this paper proposes an adaptive transfer learning framework to tackle this issue. Specifically, the framework leverages diverse source domains, employing model-level regularizers to quantify domain discrepancies and an adaptive parameter regulation mechanism to dynamically align source domains with the target domain. Embedded within the framework, a unique deep learning architecture with attention mechanisms is proposed, capable of identifying complex temporal patterns and hierarchical features in HVAC systems. Experiments on public HVAC datasets demonstrate the generalization, accuracy and robustness of our methodology under diverse data-scarce scenarios.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2815-2825"},"PeriodicalIF":8.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comprehensive Control Strategy for F-SOP Considering Three-phase Imbalance and Economic Operation in ISLDN 考虑 ISLDN 中三相不平衡和经济运行的 F-SOP 综合控制策略

IF 8.8 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-08-16 DOI: 10.1109/tste.2024.3444794

Xin Wang, Qi Guo, Chunming Tu, Liang Che, Zhong Xu, Fan Xiao, Tianlin Li, Leiqi Chen

引用次数: 0