Multi-dimensional analysis of air-conditioning energy use for energy-saving management in university teaching buildings

Abstract Owing to economic and technical reasons, campus buildings in south China were not equipped with air-conditioners (AC) for a long time. With the improvement in teaching conditions in south China, ACs are gradually being installed in teaching buildings, leading to soaring energy consumption. The teaching buildings constitute a large part of the total built-up areas on a campus. Due to the stochastic occupancy in teaching buildings, AC energy use is complicated, which is hard to describe quantitatively. Owing to high coupling relationships among the AC usage, indoor temperature, and energy consumption, it is hard to formulate any strategy on energy-savings management. Based on the data collected from an energy monitoring platform at a typical university in south China, typical patterns were proposed using data mining (DM) approaches. There were 6 AC usage patterns, 4 indoor temperature patterns, and 4 energy consumption patterns, all of which could represent complicated AC energy use. To propose precise energy-savings strategies for random AC usage, the coupling relationships among these components were revealed by multiple machine learning (ML) methods, including the decision tree, AdaBoost, and RandomForest. After that, the energy-saving control rules were formulated. As for short-time AC usage, “Turning off as leaving” is an effective way to save energy. The scales of classrooms need to be considered for usage with medium usage hours, while AC set temperature is a critical control parameter for long-time AC usage. These results provide support for more accurate simulation of energy consumption and efficient energy-saving management in teaching buildings.

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