globalchange  > 气候变化与战略
DOI: 10.1016/j.energy.2019.116608
论文题名:
Machine learning-based optimal design of a phase change material integrated renewable system with on-site PV, radiative cooling and hybrid ventilations—study of modelling and application in five climatic regions
作者: Zhou Y.; Zheng S.; Zhang G.
刊名: Energy
ISSN: 3605442
出版年: 2020
卷: 192
语种: 英语
英文关键词: Climate-adaptive operation ; Latent heat storage ; Machine learning ; Optimal design ; Phase change materials (PCMs) ; Robust operation
Scopus关键词: Carbon dioxide ; Cooling water ; Global warming ; Heat storage ; Heuristic algorithms ; Heuristic methods ; Hybrid systems ; Intelligent buildings ; Latent heat ; Learning systems ; Machine learning ; Optimal systems ; Optimization ; Pollution control ; Radiative Cooling ; Supervised learning ; Sustainable development ; Carbon dioxide emissions ; Climate-adaptive operation ; Generic optimization ; Heuristic optimization algorithms ; Multivariable optimization ; Optimal design ; Robust operation ; Supervised machine learning ; Phase change materials ; alternative energy ; carbon dioxide ; cooling water ; design ; machine learning ; optimization ; phase transition ; photovoltaic system ; water temperature ; China
英文摘要: The widespread application of advanced renewable systems with optimal design can promote the cleaner production, reduce the carbon dioxide emission and realise the renewable and sustainable development. In this study, a phase change material integrated hybrid system was demonstrated, involving with advanced energy conversions and multi-diversified energy forms, including solar-to-electricity conversion, active water-based and air-based cooling, and distributed storages. A generic optimization methodology was developed by integrating supervised machine learning and heuristic optimization algorithms. Multivariable optimizations were systematically conducted for widespread application purpose in five climatic regions in China. Results showed that, the energy performance is highly dependent on mass flow rate and inlet cooling water temperature with contribution ratios at around 90% and 7%. Furthermore, compared to Taguchi standard orthogonal array, the machine-learning based optimization can improve the annual equivalent overall output energy from 86934.36 to 90597.32 kWh (by 4.2%) in ShangHai, from 86335.35 to 92719.07 (by 7.4%) in KunMing, from 87445.1 to 91218.3 (by 4.3%) in GuangZhou, from 87278.24 to 88212.83 (by 1.1%) in HongKong, and from 87611.95 to 92376.46 (by 5.4%) in HaiKou. This study presents optimal design and operation of a renewable system in different climatic regions, which are important to realise renewable and sustainable buildings. © 2019
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/159510
Appears in Collections:气候变化与战略

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作者单位: Department of Building Services Engineering, Faculty of Construction and Environment, The Hong Kong Polytechnic University, Hong Kong, Hong Kong Special Administrative Region, China; Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, Hong Kong Special Administrative Region, China; National Center for International Research Collaboration in Building Safety and Environment, Hunan UniversityHunan 410082, China; College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China

Recommended Citation:
Zhou Y.,Zheng S.,Zhang G.. Machine learning-based optimal design of a phase change material integrated renewable system with on-site PV, radiative cooling and hybrid ventilations—study of modelling and application in five climatic regions[J]. Energy,2020-01-01,192
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