Usage behavior characteristics of household air-conditioners during the extremely hot summer – A case study of Chongqing. (15th April 2023)

• Record Type: Journal Article
• Authors: Dai, Longkang; Li, Ziqiao; Chen, Xiaoyi; Zhai, Yingxian; Fu, Chenghao; Liu, Zhilong; Liu, Meng
• Is Part Of: Building and environment. Volume 234(2023)
• Publication Date: 2023-04-15
• Subjects: Usage behavior -- Household air conditioner -- Extremely hot summer -- Cooling demand -- Hot summer and cold winter zone; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2023.110160
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: Heatwave events have become more frequent recently due to global warming issues. Chongqing suffered an extremely hot summer in 2022, leading to severe energy instability and economic loss. Cooling energy demand plays an essential role in the proportion of energy consumption in summer and highly depends on the usage behavior of air conditioners (AC). However, the previous studies rarely clarify the differential characteristics of AC usage behavior during the extremely hot summer compared with the normal summer. This study uses the k-means algorithm and statistical analysis to find out the differences in AC usage behavior in the extremely hot summer and normal summer based on the operation data of 342 ACs. The results show the main differences in the "Duration time" and "Turning-on frequency" features in the rest-time period. Specifically, in the extremely hot summer, the median duration time was reduced by 10–20% on a typical day, and the turning-on action in the before-dawn period would be more frequent. Furthermore, the peak probability of turning-on action would delay 3 h to 0 a.m. at night in the rest-time period. For the temperature setpoint, the main range (25%–75%) became 1–2 °C wider during the extremely hot summer. Highlights: A comparative analysis in ACs usage behavior is carried out between the extremely hot summer and the normal summer. Using k-means clustering algorithm to analyze the main modes of usage behavior in summer. In the extremely hot summer, the turn-on action would be more frequent and the median duration time of use will reduce.