A Network-Based Strategy to Increase the Sustainability of Building Supply Air Systems Responding to Unexpected Temperature Patterns

Abstract

As real-time indoor thermal data became available, the precision of the building thermal control systems has improved, but the use of resources has also increased. Therefore, it is imperative to examine the optimized point of energy use and thermal dissatisfaction for their efficient control. The aim of this research is to find an energy-efficient thermal control strategy to suppress the increase in thermal dissatisfaction. An adaptive control model utilizing the artificial neural network and the adjustment process of initial settings is proposed to examine its performance in controlling thermal supply air in terms of indoor thermal dissatisfaction and energy use. For a clear comparison, the standard deviation of each thermal dissatisfaction value and the weekly heating energy transfer are used. The proposed model successfully performs in reducing the indoor thermal dissatisfaction level and energy use. In comparison with two deterministic models, the performance is improved in terms of the constancy of suppressing thermal dissatisfaction levels by 72.1% and the improvement in energy efficiency by 18.8%, respectively. The significance of this study Is that it is possible to improve control precision by adding only a few modules without replacing the entire existing system, and that the model’s sustainability is increased by reducing the possibility of hardware and software retrofitting in the future.

Building Space Thermal Control Model Responding to Sharp Changes in Outdoor Temperature

“…Additionally, data-driven genetic algorithms were used to improve specific regression models in the FIS model for making structure realistic, and, by means of instantly connecting user responses, adaptive structure in the ANN was utilized to improve their statistical significance of learning algorithms. In the case of learning models, research results that improve indoor thermal comfort levels by more than 3-8% compared to conventional thermostat models were confirmed, but these learning models are based on limited conditions including building geometries and operation strategies [20][21][22].…”