Comparative Analyses of Energy Efficiency between on-Demand and Predictive Controls for Buildings’ Indoor Thermal Environment

• Main Authors: Lee-Yong Sung, Jonghoon Ahn
• Format: Article
• Language: English
• Published: MDPI AG 2020-03-01
• Series: Energies
• Subjects: indoor thermal control; energy use; thermal environment; on-demand model; predictive model; artificial neural network
• Online Access: https://www.mdpi.com/1996-1073/13/5/1089

Advanced thermal control technologies have been continuously developed to complement conventional models and algorithms to improve their performance regarding control accuracy and energy efficiency. This study analyses the strengths and weaknesses of simultaneous controls for the amount of air and its temperature by use of on-demand and predictive control strategies responding to two different outdoor conditions. The framework performs the comparative analyses of an on-demand model, which reacts immediately to indoor conditions, and a predictive model, which provides reference signals derived from data learned. Two models are combined to make a comparison of how much more efficient the combined model operates than each model when abnormal situations occur. As a result, when the two models are combined, its efficiency improves from 20.0% to 33.6% for indoor thermal dissatisfaction and from 13.0% to 44.5% for energy use, respectively. This result implies that in addition to creating new algorithms to cope with any abnormal situation, combining existing models can also be a resource-saving approach.