Prediction of campus office occupancy and AC behavior based on Transformer and the collective impact on energy consumption
摘要
Occupancy and air-conditioning on/off behavior have a significant impact on the operational energy consumption of a building, and it is important to study their joint role in implementing occupant centered control strategies and reducing building energy consumption. We propose a control strategy based on the synergistic analysis of occupancy and air-conditioning on/off behavior, incorporating tolerance temperature parameters to achieve fine-grained energy-saving regulation of air-conditioning in campus offices. Transformer is selected to handle the complex temporal patterns inherent in occupancy and air conditioning on/off behavioral data due to its expertise in capturing long-range dependencies and multi-feature interactions in temporal data. A Transformer-based multi-feature multi-step input prediction model is constructed by deploying a variety of sensors to collect environmental and behavioral data during a month-long experimental test in two offices within a university in Wuhan. The model is able to predict personnel occupancy (R2 = 0.917) and air-conditioning on/off behavior (Accuracy = 0.988) with relatively high accuracy. The energy simulation results show that the strategy can save up to 62% energy compared to the conventional system operation mode. A comparison of the results of the two office reveals that rooms with greater randomness of occupancy have a greater potential for energy savings. This study can provide an innovative and practical control strategy for reducing air-conditioning energy consumption in campus offices, which can help to achieve efficient energy utilization.