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

Lee-Yong Sung and Jonghoon Ahn
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Lee-Yong Sung: Department of Architecture, Dong-A University, Busan 49315, Korea
Jonghoon Ahn: School of Architecture and Design Convergence, Hankyong National University, Anseong 17579, Korea

Energies, 2020, vol. 13, issue 5, 1-15

Abstract: 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.

Keywords: indoor thermal control; energy use; thermal environment; on-demand model; predictive model; artificial neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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