Analysis of air-conditioning usage and energy consumption in campus teaching buildings with data mining

This study was based on the real-time operation data of the air-conditioning (AC) system which were collected by the energy consumption monitoring platform in a university located in Zhejiang province from November 2016 to February 2019. With the clustering analysis, six typical AC usage patterns and four energy consumption patterns were proposed for a whole year. Two supervised machine learning methods, namely the decision tree and random forest, were used to decouple the relation of AC usage and its energy consumption, and to figure out the different energy consumption levels under different AC usage conditions. The cross-validation method was used to compare the accuracy of various machine learning algorithms. Results show that the usage hour directly influences the energy consumption. The area scale of classrooms and AC use intensity have the significant effect on energy consumption in cooling scenario. The results of this study are beneficial to energy-saving management and the simulation of energy consumption for the teaching buildings in colleges and universities. Copyright ©2020 Journal of Zhejiang University (Engineering Science). All rights reserved.