A study on energy performance of 30 commercial office buildings in Hong Kong

• Published in: Energy and buildings 2017-06, Vol.144, p.117-128
• Main Authors: Jing, Rui, Wang, Meng, Zhang, Ruoxi, Li, Ning, Zhao, Yingru
• Format: Article
• Language: English
• Subjects: Back propagation; Back propagation networks; Benchmarks; BP neural network; Buildings; Commercial buildings; Commercial office buildings; Cooling; Cooling systems; District cooling; Emission analysis; Emission measurements; Energy audits; Energy consumption; Energy efficiency; Energy management; Energy measurement; Energy performance; Energy utilization; Energy utilization index; Hong Kong; HVAC equipment; Multiple regression analysis; Neural networks; Office buildings; Power efficiency; Regression analysis
• ISSN: 0378-7788; 1872-6178
• DOI: 10.1016/j.enbuild.2017.03.042

•Establish a building energy database for 30 Hong Kong office buildings.•Analyse key energy performance indexes of EUI, CO2e and energy breakdown.•Identify the gap between design and measured COP for HVAC system.•Compare the energy performance of district cooling and in-building chiller system.•Predict building energy consumption by two modelling techniques. A study of energy performance of 30 existing commercial office buildings in Hong Kong has been carried out. Energy audits have been conducted and the data obtained from utility bills, design documents and on-site measurements have been thoroughly assessed. Energy Utilization Index (EUI) as well as carbon emission of each building have been calculated to compare with previous studies and other benchmarks. EUI study in terms of over-time evolution and by categories have been also undertaken. An energy breakdown of 30 buildings by end-user systems was derived, showing that 68% of energy on average was consumed by HVAC system, while lighting accounted for 14% and the other systems shared the else 18% of consumption. Compared with other countries, HVAC system in Hong Kong consumed the highest proportion of energy among all end-user systems. Moreover, the COP of HVAC systems have been analyzed, indicating that with an average deviation of 16% between the design and measured chiller COP for all studied buildings, the measured chiller COP can barely meet the standard issued by the government. A district cooling system for a group of office buildings has been investigated as well, demonstrating better chiller and system efficiencies but resulting in the over-capacity installation of cooling units. To further predict building energy consumption, a mathematical model based on the measured data has been built through the multiple regression analysis and verified by the back-propagation neural network algorithm. This research is expected to provide theoretical guidance and practical data reference for the relevant evaluations of building energy efficiency.