A Network-based Model to Improve the Constancy of Thermal Comfort and the Use of Thermal Energy during Sudden Changes in Outdoor Temperature

Abstract

Purpose: As real-time indoor thermal data became available, the precision of building thermal control systems has been improved. However, resource usage has also increased. Therefore, examining the optimized point of energy use and thermal dissatisfaction for efficient control is imperative. This study aimed to find an energy-efficient thermal control strategy to suppress the increase in thermal dissatisfaction. Method: An adaptive control model utilizing an artificial neural network and the adjustment process of initial settings is proposed to examine the performance in controlling thermal air supply in terms of indoor thermal dissatisfaction and energy use. The standard deviation of each thermal dissatisfaction value and the weekly heating energy transfer are used for a clear comparison. Results: The proposed model successfully reduced indoor thermal dissatisfaction levels and energy use. Compared with the two deterministic models, the performance is improved in terms of the constancy of suppressing thermal dissatisfaction levels by 95.3% and the improvement of energy efficiency by 3.7%. The model can improve the sustainability of the old thermal system without replacing the overall system.