Mitigation imbalance distribution: Data augmentation of local small sample for building electricity load in time-series generative adversarial network DOI
Shengdong Zhang, Jiangjiang Wang, Zhiqiang Yin

и другие.

Journal of Building Engineering, Год журнала: 2024, Номер 99, С. 111549 - 111549

Опубликована: Дек. 9, 2024

Язык: Английский

Smart Building Transferable Energy Scheduling Employing Reward Shaping Deep Reinforcement Learning with Demand Side Energy Management DOI

Siva Subramanian Kumaresan,

Pandia Rajan Jeyaraj

Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 112316 - 112316

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

1
Economic, Policy, Social, and Regulatory Aspects of AI-Driven Smart Buildings DOI

M. Arun,

Debabrata Barik,

Sreejesh S.R. Chandran

и другие.

Journal of Building Engineering, Год журнала: 2024, Номер unknown, С. 111666 - 111666

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

5
An efficient meteorological index for estimating building latent cooling load based on coupled temperature and humidity DOI

Jingfu Cao,

Mingcai Li,

Ruixue Zhang

и другие.

Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 111933 - 111933

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Optimizing Urban Block Morphology for Energy Efficiency and Photovoltaic Utilization: Case Study of Wuhan DOI Creative Commons

Ruoyao Wang,

Yanyan Huang, Guoliang Zhang

и другие.

Buildings, Год журнала: 2025, Номер 15(7), С. 1118 - 1118

Опубликована: Март 29, 2025

With global carbon emissions continuing to rise and urban energy demands growing steadily, understanding how block morphology impacts building photovoltaic (PV) efficiency consumption has become crucial for sustainable development climate change mitigation. Current research primarily focuses on individual optimization, while block-scale coupling relationships between PV utilization remain underexplored. This study developed an integrated prediction optimization tool using deep learning physical simulation assess design parameters (building morphology, orientation, layout) affect performance. Through a methodology combining modeling, potential assessment, simulation, the quantified parameters, utilization, consumption. Results demonstrate that appropriate forms layouts reduce shadow obstruction, enhance system capability, simultaneously improve reducing The provides improved accuracy, enabling planners scientifically maximize generation minimize use. Extensive experimental validation demonstrates model analytical methods proposed in this will help break through limitations of research, making PV-energy analysis at scale possible, providing scientific basis achieving low-carbon transformation sector.

Язык: Английский

Процитировано

0
A Comprehensive Review of Building Energy Optimization Using Metaheuristic Algorithms DOI

Mohammad Ali Karbasforoushha,

Mohammad Khajehzadeh, Thira Jearsiripongkul

и другие.

Journal of Building Engineering, Год журнала: 2024, Номер unknown, С. 111377 - 111377

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

2
Mitigation imbalance distribution: Data augmentation of local small sample for building electricity load in time-series generative adversarial network DOI
Shengdong Zhang, Jiangjiang Wang, Zhiqiang Yin

и другие.

Journal of Building Engineering, Год журнала: 2024, Номер 99, С. 111549 - 111549

Опубликована: Дек. 9, 2024

Язык: Английский

Процитировано

0