A Heuristic Rule-Based Passive Design Decision Model  for Reducing Heating Energy Consumption of Korean  Apartment Buildings

Suh, Dongjun; Chang, Sun Hyok

doi:10.3390/en7116897

Cited by 7 publications

(13 citation statements)

References 15 publications

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“…The housing direction is the direction that the front of the building faces. In South Korea, it is usually determined based on the position of the living room [26]. This is because Korean users spend a considerable amount of time in the living room.…”

Section: Analysis Process (Multi-collinearity Outliers and Dependenmentioning

confidence: 99%

Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea

2019

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When researching the energy consumption of residential buildings, it is becoming increasingly important to consider how residents use energy. With the advancement of computing power and data analysis techniques, it is now possible to analyze user information using big data techniques. Here, we endeavored to integrate user information with the physical characteristics of residential buildings to analyze how these elements impact energy consumption. Regression analysis was conducted to accurately identify the impact of each element on energy consumption. It was found that six elements were influential in all seasons: the number of exterior walls, housing direction, housing area, number of years occupied, number of household members, and the occupation of the household head. The elements that had an impact in each period were then derived. Based on the results of the regression analysis, input variables for the training of an artificial neural network (ANN) model were selected for each period, and residential energy consumption prediction models were implemented based on actual consumption. The elements identified as those affecting energy consumption, through regression analysis, can be used for implementing prediction models with advanced forms. This study is significant in that we derived influential elements from an integrative perspective.Sustainability 2020, 12, 109 2 of 28 Thus far, research on how the physical environment affects energy consumption has been intensively conducted; however, it is difficult to collect a large amount of user information owing to its personal nature. It is also difficult to quantitatively reduce and analyze the information on individuals. Nevertheless, the analysis of qualitative data has become possible through national surveys and new methodologies that are capable of nonlinear analysis, such as artificial neural networks (ANNs). In this study, the energy consumption in residential buildings in South Korea was investigated, by integrating the physical characteristics of residential buildings with information on occupant behavior and characteristics, to identify elements that affect residential energy consumption. The elements that were found to be influential were subsequently used to construct energy consumption prediction models. Literature ReviewMost studies on the energy consumption of residential buildings have focused on physical characteristics, and those that considered user behavior only considered one or two user elements. However, user information cannot be defined with such few elements; therefore, studies that reflect user information from various perspectives are required. In this study, elements that affect energy consumption were derived considering both the physical characteristics of buildings and user information. Effects of Physical Building Characteristics on Energy ConsumptionKim [4] reported that annual power consumption varied depending on the housing type, construction year, number of floors, building structure, and building location. It was found that ...

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Section: Analysis Process (Multi-collinearity Outliers and Dependenmentioning

confidence: 99%

Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea

2019

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“…Due to the escalating electricity prices and growing awareness of the damaging effects from greenhouse gases to the environment, construction design teams are concentrating on delivering cost effective yet environmentally sustainable designs. For a designer to achieve the above criteria, they must understand all factors that will affect building performance [40].…”

Section: Two Three Fourmentioning

confidence: 99%

Cost Implication of Implementing External Facade Systems for Commercial Buildings

2018

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The significance of cost effectiveness in sustainable design has heightened over the past ten years due to the need for reducing building operational expenses and improving environmental quality. This paper determines life-cycle cost over a 20-year period for eleven external facade systems from seven Australian capital cities. Factors affecting life-cycle expenditure in implementing external facade systems of commercial buildings are considered, including construction cost, space heating and cooling cost, maintenance cost and rental loss due to the thickness of external facade systems. The most cost-effective external facade system is identified for each city. Double-glazing facade is found to be the most cost-effective system in Sydney; however, masonry-veneer facade performs the best overall. This paper can provide insight for commercial design teams in designing buildings with cost-effectiveness and sustainable throughout its life-cycle and highlight the importance of a holistic cost review at design stage in ensuring client satisfaction.

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“…Regarding research on building design, Nair et al [5] explored factors influencing the adoption of energy-efficiency measures in Swedish homeowners, revealing that income, education, age, and contextual factors influenced homeowners' preferences for specific measures. Suh et al [6] established heuristic rules for energy-efficient building design in Korea based on a sensitivity analysis, offering valuable guidelines for architects and engineers to develop sustainable apartment housing. Ling et al [7] analyzed the impact of apartment location on space heating consumption in multi-apartment buildings, providing location correction factors for fair heating cost allocation in different regions and building types in China.…”

Section: Introductionmentioning

confidence: 99%

A Simultaneous Usage Ratio Based on Occupant Behavior: A Case Study of Intermittent Heating in an Apartment Building in Japan

Kikuta,

Abe

2024

Buildings

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It is important to reduce the building load and downsize the heat source equipment capacity during construction or renovation carried out toward the achievement of carbon neutrality by 2050 in Japan. However, this sometimes results in the oversizing of the heat source equipment capacity, despite the fact that designers are engaged in the implementation of safety designs while attempting to balance between cost and risk. This study investigated the simultaneous usage ratio of heating based on occupant behavior in an apartment building with the aim of optimizing this capacity. This ratio was defined as a peak load-based approach rather than simultaneity based on the number of people using the system. First, the analysis was conducted for the heating load characteristics for each dwelling unit and each household composition. The subject of this case study was an apartment building located in Sapporo, Japan. Based on these data, a method for creating the curve of the simultaneous usage ratio to avoid a combinatorial explosion was suggested. As a result, the ratio created for about 200 dwelling units was 53.6% in an apartment building and generally stabilized when the number of dwelling units exceeded 30. Finally, a case study was attempted to analyze the influence of changes in household composition on the ratio. If the method proposed in this study for creating the curve of simultaneous usage ratios were to be applied in not only this case study but also in case studies of non-residential buildings such as offices, new results about the curves of ratios that differ from those of apartment buildings could be obtained. Therefore, this case study provides a methodology for statistically quantifying the simultaneous usage ratio as one of the factors in determining the appropriate heat source equipment capacity in the design stage.

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A Heuristic Rule-Based Passive Design Decision Model for Reducing Heating Energy Consumption of Korean Apartment Buildings

Cited by 7 publications

References 15 publications

Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea

Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea

Cost Implication of Implementing External Facade Systems for Commercial Buildings

A Simultaneous Usage Ratio Based on Occupant Behavior: A Case Study of Intermittent Heating in an Apartment Building in Japan

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