An Adaptive Control Model for Thermal Environmental Factors to Supplement the Sustainability of a Small-Sized Factory

Abstract

Effective indoor thermal controls can have quantifiable advantages of improving energy efficiency and indoor environmental quality, which can also lead to additional benefits such as better workability, productivity, and economy in buildings. However, in the case of factory buildings whose main usage is to produce and process goods, securing thermal comfort for their workers has been regarded as a secondary problem. This study aims to explore the method for cooling and heating air supply controls to improve the thermal comfort of factory buildings by use of a data-driven adaptive model. The genetic algorithm using the idea of occupancy rate helps the model to effectively analyze the indoor environment to determine the optimized conditions for energy use and thermal comfort. As a result, the proposed model successfully shows better performance, which confirms that there is a 2.81% saving in energy consumption and a 16–32% reduction in indoor thermal dissatisfaction. In particular, the significance of this study is that energy use and thermal dissatisfaction can be reduced simultaneously despite precise air-supply controls that are performed in response to the conditions of the building, weather, and occupancy rate.