Xiaoyu Jin

Xiaoyu Jin
The Hong Kong Polytechnic University | PolyU · Department of Building Services Engineering

Master of Science

About

8
Publications
1,903
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102
Citations

Publications

Publications (8)
Article
Full-text available
Distributed energy resources (DERs) would play a crucial role in the transition towards decentralized and decarbonized energy systems. However, due to the limited availability of long-term, high-resolution datasets, there has been little research on the descriptive analysis of distributed energy systems throughout the lifespan of distributed power...
Article
Full-text available
The building sector plays a crucial role in the worldwide decarbonization effort, accounting for significant portions of energy consumption and environmental effects. However, the scarcity of open data sources is a continuous challenge for built environment researchers and practitioners. Although several efforts have been made to consolidate existi...
Preprint
Full-text available
The building sector plays a crucial role in the worldwide decarbonization effort, accounting for significant portions of energy consumption and environmental effects. However, the scarcity of open data sources is a continuous challenge for built environment researchers and practitioners. Although several efforts have been made to consolidate existi...
Article
The spatial feature of building energy consumption in a city is essential for urban level energy planning and policy making. With the increasing availability of urban level building energy benchmarking datasets, machine learning has shown a powerful capability of making data-driven predictions on urban level building energy consumption. However, th...
Article
Full-text available
Building energy performance benchmarking is adopted by many countries in the world as an effective tool to reduce energy consumption at city or country level. Machine learning holds a lot of promise for quickly and correctly predicting energy consumption from massive data, thereby it’s suitable for large-scale performance assessment. However, there...
Conference Paper
Full-text available
Machine learning holds a lot of promise for quickly and correctly assessing building energy performance at urban level. However, due to the lack of data for minority types of buildings, unfavorable results are produced sometimes. Therefore, this study proposes a concise approach to generate enough data for training machine learning models while avo...

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