@article {31306,
	title = {Clustering and statistical analyses of air-conditioning intensity and use patterns in residential buildings},
	journal = {Energy and Buildings},
	volume = {174},
	year = {2018},
	month = {09/2018},
	pages = {214 - 227},
	abstract = {<p>Energy conservation in residential buildings has gained increased attention due to its large portion of global energy use and potential of energy savings. Occupant behavior has been recognized as a key factor influencing the energy use and load diversity in buildings, therefore more realistic and accurate air-conditioning (AC) operating schedules are imperative for load estimation in equipment design and operation optimization. With the development of sensor technology, it became easier to access an increasing amount of heating/cooling data from thermal energy metering systems in residential buildings, which provides another possible way to understand building energy usage and occupant behaviors. However, except for cooling energy consumption benchmarking, there currently lacks effective and easy approaches to analyze AC usage and provide actionable insights for occupants. To fill this gap, this study proposes clustering analysis to identify AC use patterns of residential buildings, and develops new key performance indicators (KPIs) and data analytics to explore the AC operation characteristics using the long-term metered cooling energy use data, which is of great importance for inhabitants to understand their thermal energy use and save energy cost through adjustment of their AC use behavior. We demonstrate the proposed approaches in a residential district comprising 300 apartments, located in Zhengzhou, China. Main outcomes include: Representative AC use patterns are developed for three room types of residential buildings in the cold climate zone of China, which can be used as more realistic AC schedules to improve accuracy of energy simulation; Distributions of KPIs on household cooling energy usage are established, which can be used for household AC use intensity benchmarking and performance diagnoses.</p>},
	keywords = {AC usage benchmarking, Air-conditioning, Clustering analysis, KPIs, residential building, Use pattern},
	issn = {03787788},
	doi = {10.1016/j.enbuild.2018.06.035},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0378778818307199https://api.elsevier.com/content/article/PII:S0378778818307199?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0378778818307199?httpAccept=text/plain},
	author = {Jingjing An and Da Yan and Tianzhen Hong}
}
