Improving sustainability of ever-changing building spaces affected by users’ fickle taste: A focus on human comfort and energy use

Moon, Jin Woo; Ahn, Jonghoon

doi:10.1016/j.enbuild.2019.109662

Cited by 27 publications

(14 citation statements)

References 32 publications

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“…A building's plane shape is a major factor that alters the outer wall area, which, in turn, can affect the heating and cooling loads. Plane types are investigated via physical and typological classification [47,48]. e physical space classification method is used to classify plane types according to room composition, i.e., the linkage of living room (L), dining room (D), and kitchen (K), and plane shape, whereas the typological classification method is used to classify plane types according to the plane's shape or elevation.…”

Section: Architecture Variable Designmentioning

confidence: 99%

Verification of Energy Usage Based on Standard Building Model Development of Low‐Rise Residential Buildings in South Korea

Kim¹,

Yoo

Choi

et al. 2021

Advances in Civil Engineering

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The energy consumption of low-rise residential buildings in South Korea exceeds the targets set in national policies and the standards of other countries. Moreover, there are insufficient policies in place to improve the energy performance of existing low-rise residential buildings and no means to investigate the current status. A standard model enables cost-effective and fast load forecasting and can also be used to establish long-term policies through evaluation of energy saving in buildings before and after the application of energy policies. This study developed a standard model for predicting energy consumption by reflecting the characteristics of low-rise residential buildings in Korea. The standard model was developed based on reliable related standards, national statistical data, and national reports, and the energy variables applied were validated through a sensitivity analysis. Surveys and field measurements were conducted to investigate the energy usage of 70 households in low-rise residential buildings in Korea, and the developed model was validated through comparison with the actual energy usage data. Consequently, the total energy consumption error rate was 12.67% (R2 value: 0.8164), with a significance level higher than 80%, which indicated that the developed model was highly efficient and reliable.

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Section: Architecture Variable Designmentioning

confidence: 99%

Verification of Energy Usage Based on Standard Building Model Development of Low‐Rise Residential Buildings in South Korea

Kim¹,

Yoo

Choi

et al. 2021

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…In some algorithms, mixing methods of dampers and resistance coils were tested through the experimental data and data-driven regression to rapidly react thermal demands connecting some different building geometries and climate conditions. Specific models tested the control efficiency of combining the FIS and ANN models for operating dampers and diffusers in a single duct work and distribution networks [15][16][17].…”

Section: Building Thermal Controlsmentioning

confidence: 99%

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

Ahn

2020

Sustainability

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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.

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“…As effective components of dampers and resistance coils, they were examined to build auxiliary systems complementing main HVAC systems with newly analyzing multivariate regression models derived from situational and seasonal operation results. Connecting various building geometries and climate conditions, some systematic assumptions reflecting unpredictable occupant behaviors were investigated to define improved control or network strategies in combining the FIS and the ANN algorithms [14,15].…”

Section: Thermal Systems In Buildingsmentioning

confidence: 99%

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

Ahn

2020

Sustainability

Self Cite

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In thermal controls in buildings, recent statistical and data-driven approaches to optimize supply air conditions have been examined in association with several types of building spaces and patterns of energy consumption. However, many strategies may have some problems where high-control precision may increase energy use, or low energy use in systems may decrease indoor thermal quality. This study investigates a neural network algorithm with an adaptive model on how to control the supply air conditions reflecting learned data. During the process, the adaptive model complements the signals from the network to independently maintain the comfort level within setting ranges. Although the proposed model effectively optimizes energy consumption and supply air conditions, it achieves quite improved comfort levels about 14% more efficient than comparison models. Consequently, it is confirmed that a network and learning algorithm equipped with an adaptive controller properly responds to users’ comfort levels and system’s energy consumption in a single space. The improved performance in space levels can be significant in places where many spaces are systematically connected, and in places which require a high consistency of indoor thermal comfort. Another advantage of the proposed model is that it properly reduces an increase in energy consumption despite an intensive strategy is utilized to improve thermal comfort.

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Improving sustainability of ever-changing building spaces affected by users’ fickle taste: A focus on human comfort and energy use

Cited by 27 publications

References 32 publications

Verification of Energy Usage Based on Standard Building Model Development of Low‐Rise Residential Buildings in South Korea

Verification of Energy Usage Based on Standard Building Model Development of Low‐Rise Residential Buildings in South Korea

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

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

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