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

Abstract

For the sustainable use of building spaces, various methods have been studied to satisfy specific conditions required by the characteristics of space types and the energy use in operation. However, several effective control approaches adopting the latest statistical tools may have problems such as higher control precision increases energy consumption, or lower energy consumption decreases their control precision. This study proposes an optimized model to reach the indoor set-point temperature by controlling the amount of heating supply air and its temperature and investigates the efficiency of an adaptive controller to maintain indoor thermal comfort within setting ranges. In the consistency of the comfort level, the fuzzy logic controller was found to be 1.76% and the artificial neural network controller to be 17.83%, respectively, more efficient than the conventional thermostat. In addition, for energy use efficiency, both of the controllers were confirmed to be over 3.0% more efficient. Consequently, the network-based controller with the adaptive controller checking comfort levels effectively works to improve both energy efficiency and thermal comfort. This improvement can be significant in places such as commercial high-rises, large hospitals, and data centers where many spaces are intensively woven with appropriate thermal environments to maintain users’ workability.

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

“…It can be assumed that these relatively small differences in energy consumed were derived from its precise control by optimizing the overshooting value generated when the Trm reached the Tset. In the simulation tests conducted with similar methodologies and models, thermal comfort and energy use show the control efficiency by 6~14% and 2~39%, respectively [14,17,31]. However, since this model tests an adaptive model using additional modules to maintain the quality of comfort in existing control systems, the energy use savings of about 5% can be sufficiently significant.…”

“…These indices dealing with major thermal conditions and human factors have been developed as the architectural and user characteristics were regularized by the increases in experimental data and simulated genetic algorithms [15, Technical Gazette 30, 3(2023), 815-823 16]. By using the methods, several assumptions and design scenarios were tested to define better rules of tuning algorithms for the comfort models [17].…”

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.…”

Abatement of the Increases in Cooling Energy Use during a Period of Intense Heat by a Network-Based Adaptive Controller

“…The combination of the FIS and the ANN algorithms was tested to improve the validity of regression models and the deterministic effectiveness by the use of multilayer structures. The combined models have been able to solve complex strategies that depend on the number of data and deterministic processes, such as combustion in power plants, friction loss in distribution networks, and kinetic façade in building envelopes [12,13]. As these approaches become available, many experimental models, including several outliers and technical errors, were investigated to define better-fitted models rather than one-directional analyses in the past.…”