Determining air-conditioning usage patterns in Singapore from distributed, portable sensors

• Published in: Energy procedia Vol. 122; pp. 313 - 318
• Main Authors: Happle, Gabriel, Wilhelm, Erik, Fonseca, Jimeno A., Schlueter, Arno
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2017
• Subjects: Big Data; Distributed Sensor Data; Distribution-based Clustering Air-Conditioning; IOT; User Behavior; Distribution-based Clustering Air-Conditioning; Big Data; User Behavior; Distributed Sensor Data; IOT
• ISSN: 1876-6102; 1876-6102
• DOI: 10.1016/j.egypro.2017.07.328

In Singapore, a large portion of residential electricity consumption is typically used for space cooling. The operation patterns and respective energy consumption of residential split system air-conditioning (AC) units depend on residents’ behavior and are not well known. Singapore's National Science Experiment (NSE) is a nation-wide experiment that involves students carrying a small sensor device to collect environmental data. Over 43,000 students participated in 2015, and the experiment is still ongoing. In this study, we use the Expectation-Maximization algorithm to fit a two component Gaussian mixture model to measured data of temperature, humidity, and pressure to infer patterns of AC exposure. The daily probability profile of AC exposure of Singaporean students was determined. The results can be used as a basic dataset for thermal comfort and energy consumption behavior studies. Derived patterns of AC usage could be used in building energy simulation.