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

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.

Artificial Intelligence (AI)-Based Technology Adoption in the Construction Industry: A Cross National Perspective Using the Technology Acceptance Model

“…Nevertheless, as these technologies bring numerous advantages, they also raise new challenges, notably in terms of safety and ethics. Safety enhancements have been notable, with advancements in computer vision and Long Short-Term Memory (LSTM) facilitating the development of predictive models for unsafe behaviours in construction sites [42,43]. However, with increasing automation comes ethical considerations.…”

“…These advancements have led to real-time data analysis, technical procedure automation, and an overall enhancement of construction process efficiency. This integration has also contributed to novel developments in niche sectors such as structural glass engineering [45] and building energy efficiency [35,36,43].…”

Performance Analyses of Temperature Controls by a Network-Based Learning Controller for an Indoor Space in a Cold Area

“…Various design scenarios for indoor thermal conditions derived from architectural components, thermal conditions, and users' characteristics were considered to define much more reliable calculation for the PMV levels. By use of modified genetic algorithms derived from experimental data, several functions for the ambiguous situations were developed to maintain the level of buildings' energy performance [25][26][27].…”

Improvement of the Performance Balance between Thermal Comfort and Energy Use for a Building Space in the Mid-Spring Season

“…In many cases of modified approaches to effectively define meaningful interactions in the human factors, their inner structures of thermal systems and architectural components were investigated to compare mechanical functions with data-driven regression results for more reliable network-based models. Modified algorithms derived from experimental and presimulated data, in order to define several functions for several distinct situations, were examined to complement existing mechanical rules for energy supply and regression models for the PMV [22][23][24]. For more precise models in combining methods, cosimulation applications based on programming language were utilized to deal with the communication between thermal calculation and programming modules were to conduct their real-time correction reflecting analyses of predicted and resulted values in calculating thermal demands.…”