scholarly journals Analysis of Heating and Cooling Loads of Electrochromic Glazing in High-Rise Residential Buildings in South Korea

2018 ◽  
Vol 10 (4) ◽  
pp. 1121 ◽  
Author(s):  
Myunghwan Oh ◽  
Sungho Tae ◽  
Sangkun Hwang
Keyword(s):  
South Korea ◽  
Residential Buildings ◽  
High Rise ◽  
Heating And Cooling ◽  
Cooling Loads

Energy Performance Analysis on the Design Conditions of High-Rise Apartment Houses in South Korea

2014 ◽  
Vol 1025-1026 ◽  
pp. 1099-1102 ◽  
Author(s):  
Hae Kwon Jung ◽  
Ki Hyung Yu ◽  
Young Sun Jeong
Keyword(s):  
South Korea ◽  
Thermal Insulation ◽  
Energy Demand ◽  
Residential Buildings ◽  
Building Envelope ◽  
Apartment House ◽  
High Rise ◽  
Heating And Cooling ◽  
Functional Efficiency ◽  
Cooling Energy Demand

Aapartment houses account for more than 60% of the total of residential buildings to be built in South Korea. In particular, a high-rise apartment house with 21 floors or more has steadily increased in densely populated areas. The heating and cooling energy demand of the apartment house is greatly affected by the shape and the thermal insulation of its building envelope. In addition to its functional efficiency, the shape of building envelope in a high-rise apartment house is considered to be an important factor for the urban landscape with diverse construction methods and materials. In this study, we analyzed the heating and cooling energy demand depending on the effective heat capacity of building structure and the installation position of thermal insulation materials as the design conditions of high-rise apartment houses. This study used the ECO2 energy analysis program for the building energy efficiency grading certification system in South Korea.

scholarly journals Towards Sustainable Energy Retrofitting, a Simulation for Potential Energy Use Reduction in Residential Buildings in Palestine

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3876
Author(s):  
Sameh Monna ◽  
Adel Juaidi ◽  
Ramez Abdallah ◽  
Aiman Albatayneh ◽  
Patrick Dutournie ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Energy Savings ◽  
Residential Buildings ◽  
Residential Building ◽  
Simulation Models ◽  
Water Heating ◽  
Heating And Cooling ◽  
Space Cooling ◽  
Cooling Loads

Since buildings are one of the major contributors to global warming, efforts should be intensified to make them more energy-efficient, particularly existing buildings. This research intends to analyze the energy savings from a suggested retrofitting program using energy simulation for typical existing residential buildings. For the assessment of the energy retrofitting program using computer simulation, the most commonly utilized residential building types were selected. The energy consumption of those selected residential buildings was assessed, and a baseline for evaluating energy retrofitting was established. Three levels of retrofitting programs were implemented. These levels were ordered by cost, with the first level being the least costly and the third level is the most expensive. The simulation models were created for two different types of buildings in three different climatic zones in Palestine. The findings suggest that water heating, space heating, space cooling, and electric lighting are the highest energy consumers in ordinary houses. Level one measures resulted in a 19–24 percent decrease in energy consumption due to reduced heating and cooling loads. The use of a combination of levels one and two resulted in a decrease of energy consumption for heating, cooling, and lighting by 50–57%. The use of the three levels resulted in a decrease of 71–80% in total energy usage for heating, cooling, lighting, water heating, and air conditioning.

scholarly journals Impact of urban heat island effect on the heating and cooling loads of residential buildings in Tianjin City, China

2020 ◽  
Vol 39 (8) ◽  
pp. 1296-1307
Author(s):  
Fanchao MENG ◽  
Guoyu REN ◽  
Jun GUO ◽  
Lei ZHANG ◽  
Ruixue ZHANG ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Residential Buildings ◽  
Urban Heat Island Effect ◽  
Heat Island ◽  
Heating And Cooling ◽  
Island Effect ◽  
Urban Heat ◽  
Tianjin City ◽  
Cooling Loads

scholarly journals Mobile application tool for individual maintenance users on high-rise residential buildings in South Korea

2018 ◽  
Vol 167 ◽  
pp. 01002 ◽  
Author(s):  
Hee-Sung Cha ◽  
Jun Kim ◽  
Do-Hee Kim ◽  
Jia Shin ◽  
Kun-Hee Lee
Keyword(s):  
South Korea ◽  
Mobile Application ◽  
Residential Buildings ◽  
Prototype System ◽  
Repair Work ◽  
High Rise ◽  
Building Maintenance ◽  
Rapid Economic Growth ◽  
Feedback Structure ◽  
Online Feedback

Since 1980’s, the rapid economic growth resulted in so many aged apartment buildings in South Korea. Nevertheless, there is insufficient maintenance practice of buildings. In this study, to facilitate the building maintenance the authors classified the building defects into three levels according to their level of performance and developed a mobile application tool based on each level’s appropriate feedback. The feedback structure consisted of ‘Maintenance manual phase’, ‘Online feedback phase’, ‘Repair work phase of the specialty contractors’. In order to implement each phase the authors devised the necessary database for each phase and created a prototype system that can develop on its own. The authors expect that the building users can easily maintain their buildings by using this application.

Prediction and Analysis of Building Energy Efficiency Using Artificial Neural Network and Design of Experiments

2016 ◽  
Vol 819 ◽  
pp. 541-545 ◽  
Author(s):  
Sholahudin ◽  
Azimil Gani Alam ◽  
Chang In Baek ◽  
Hwataik Han
Keyword(s):  
Neural Network ◽  
Energy Efficient ◽  
Residential Buildings ◽  
Building Energy ◽  
Cooling Load ◽  
Simulation Data ◽  
Heating And Cooling ◽  
Heating Load ◽  
Input Variables ◽  
Cooling Loads

Energy consumption of buildings is increasing steadily and occupying approximately 30-40% of total energy use. It is important to predict heating and cooling loads of a building in the initial stage of design to find out optimal solutions among various design options, as well as in the operating stage after the building has been completed for energy efficient operation. In this paper, an artificial neural network model has been developed to predict heating and cooling loads of a building based on simulation data for building energy performance. The input variables include relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution of a building, and the output variables include heating load (HL) and cooling load (CL) of the building. The simulation data used for training are the data published in the literature for various 768 residential buildings. ANNs have a merit in estimating output values for given input values satisfactorily, but it has a limitation in acquiring the effects of input variables individually. In order to analyze the effects of the variables, we used a method for design of experiment and conducted ANOVA analysis. The sensitivities of individual variables have been investigated and the most energy efficient solution has been estimated under given conditions. Discussions are included in the paper regarding the variables affecting heating load and cooling load significantly and the effects on heating and cooling loads of residential buildings.

Fabrication Processes of Residential Building Adopted Cruse Housing System

2014 ◽  
Vol 638-640 ◽  
pp. 1606-1609
Author(s):  
Jae Min Shin ◽  
Gwang Hee Kim
Keyword(s):  
South Korea ◽  
Urban Area ◽  
Residential Buildings ◽  
Residential Building ◽  
Construction Method ◽  
Housing System ◽  
Land Price ◽  
High Rise ◽  
Modular Buildings ◽  
Construction System

In South Korea, the need for residential modular buildings has highlighted, due to the increase in demand for small housing and the high land price in urban area. Thus, the cruse housing system (CHS) was developed to build high-rise residential buildings. The object of this study is to analyze the characteristics and fabrication processes of CHS residential buildings when the in-fill construction method is adopted. The result of this study showed that there is the potential to utilize the fabrication processes of CHS in-fill construction system to build high-rise modular buildings.

The effect of slat angle of windows with venetian blinds on heating and cooling loads of buildings in South Korea

Energy ◽  
1995 ◽  
Vol 20 (12) ◽  
pp. 1225-1236 ◽  
Author(s):  
Sung-Hwan Cho ◽  
Kee-Shik Shin ◽  
M. Zaheer-Uddin
Keyword(s):  
South Korea ◽  
Heating And Cooling ◽  
Venetian Blinds ◽  
Cooling Loads

scholarly journals Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

2020 ◽  
Vol 10 (11) ◽  
pp. 3829 ◽  
Author(s):  
Arash Moradzadeh ◽  
Amin Mansour-Saatloo ◽  
Behnam Mohammadi-Ivatloo ◽  
Amjad Anvari-Moghaddam
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Residential Buildings ◽  
Load Forecasting ◽  
Training Data ◽  
Support Vector ◽  
Cooling Load ◽  
Heating And Cooling ◽  
Cooling Loads ◽  
Heating And Cooling Load

Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.

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