• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.2
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• pp.3-11
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• 2018

Korea has achieved a rapid economic development and with the increase in population and national income and the expansion of social and economic activities, energy consumption has rapidly increased too. Energy consumption per head has constantly increased and currently, power consumption per head is 7.5 times bigger than in 1985. Buildings occupy 25% of total energy consumption and especially, 50% of total energy is consumed for heating and cooling. In this situation, multi-family housing, which has constantly been increased, has an energy saving rate of 1.9%, which is the lowest level and this makes the government's energy policy for sustainable energy system development useless. Besides, energy consumption leads to secondary problems, such as air, water and marine pollution and heat pollution and wastewater/drainage and the increased use of fossil fuel is a fundamental reason for ozone layer destruction and global warming. Therefore, efficient energy consumption plans are required. This study aims to analyze energy performance in each block type of high-rise and diversified multi-family housing that accounts for 60% of all the housing forms, depending on the variations in stories through BIM-based energy simulation. For this study, four representative block types were selected, based on the multi-family floor plan, which is certified for energy performance evaluation and they were applied to the floor plan of a multi-family house that is scheduled to be built. Then BIM modeling was conducted from the fifth story to the 40th story at an intervals of 5 stories and based on the finding, energy characteristics of each block type and energy performance depending on the variations in stories were analyzed. It is considered that this would serve as objective data for block type and block story decision of energy performance-based multi-family housing.

• 한국태양에너지학회:학술대회논문집
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• 2009.11a
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• pp.211-215
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• 2009

As entering in the time of high oil price, seriousness of an energy is on the rise and the importance of energy is growing. Especially, building energy occupying 24% of total demand of energy is expected to be possible to reduce energy demand more than other section. To reduce the building energy consumption, this study analyzes function and thermal performance of Super window by heat experimental apparatus. Super window is a 2-track low-e glazing window for high insulation efficiency. By applying the results of this experiment to building energy efficiency rating tool, this study compares energy efficiency rates depending on a region.-Jeju, South, Central. And it shows how much does Super window reduce Building energy consumption.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.301-307
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• 2022

Energy consumption has grown alongside dramatic population increases. Statistics show that buildings in particular utilize a significant amount of energy, worldwide. Because of this, building energy prediction is crucial to best optimize utilities' energy plans and also create a predictive model for consumers. To improve energy prediction performance, this paper proposes a ResNet-LSTM model that combines residual networks (ResNets) and long short-term memory (LSTM) for energy consumption prediction. ResNets are utilized to extract complex and rich features, while LSTM has the ability to learn temporal correlation; the dense layer is used as a regression to forecast energy consumption. To make our model more robust, we employed Huber loss during the optimization process. Huber loss obtains high efficiency by handling minor errors quadratically. It also takes the absolute error for large errors to increase robustness. This makes our model less sensitive to outlier data. Our proposed system was trained on historical data to forecast energy consumption for different time series. To evaluate our proposed model, we compared our model's performance with several popular machine learning and deep learning methods such as linear regression, neural networks, decision tree, and convolutional neural networks, etc. The results show that our proposed model predicted energy consumption most accurately.

• Journal of the Korean Solar Energy Society
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• v.30 no.4
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• pp.49-54
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• 2010

Most energy using in building part is mainly consumed for heating and cooling to meet occupancy's comfort temperature. Generally, heating energy consumption show high value than cooling energy in Korea because of high temperature difference in winter season as compared with summer in apartment building. The efforts to develope mechanical performance have been studied to reduce energy consumption in building energy field until now. However, the energy consumption in building is impacted by not only system performance but also PMV particularly at temperature and Clo value. This means that energy consumption can be changed by occupancy's comfort setting temperature in apartment building. This study investigated the passibility of overheating in apartment building by occupant' slow Clo and its setting temperature from preceding research and then the heating energy consumption by setting temperature was calculated with ESP-r. The effects of heating energy and $CO_2$ reduction are also evaluated quantitatively with Clo value. The results showed that keeping ISO-7730 standards can reduce heating energy up to 21% in compared with option 2; also, wearing underclothes with ISO-7730 standard can considerably reduce heating energy consumption up to 50%. As compared with option 2, the reduction of $CO_2$ emission for option 3 showed 0.63TCO2 of kerosene, 0.49TCO2 of LNG and 1.09TCO2 of electricity. The option 4 can be reduced by 1.48TCO2 of kerosene, 1.16TCO2 of LNG and 2.57TCO2 of electricity respectively.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1330-1333
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• 2004

The methodology for modeling building energy consumption is well established for energy saving calculation in the temperate zone both for performance-based energy retrofitting contracts and measurement and verification (M&V) projects. Mostly, statistical regression models based on utility bills and outdoor dry-bulb temperature have been applied to baseline monthly and annual whole building energy use. This paper presents the application of neural networks (NN) to model landlord energy consumption of commercial buildings in Singapore. Firstly, a brief background information on NN and its application on the building energy research is provided. Secondly, five commercial buildings with various characteristics were selected for case studies. Monthly mean outdoor dry-bulb temperature ($T_0$), Relative Humidity (RH) and Global Solar Radiation (GSR) are used as network inputs and the landlord monthly energy consumption of the same period is the output. Up to three years monthly data are taken as training data. A forecast has been made for another year for all the five buildings. The performance of the NN analysis was evaluated using coefficient of variance (CV). The results show that NNs is powerful at predicting annual landlord energy consumption with high accuracy.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.44-48
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• 2021

Semiconductor inspection equipment makes components using materials with insulating properties for functional inspection including current and voltage of semiconductor parts. A representative insulating material is plastic, and plastic is made of a component through an injection process using plastic pellet. When plastic pellets contain excessive moisture, problems such as performance degradation and product surface defects occur. To prevent this, pre-drying is essential, and the heat convective type is the most applied. However, the heat convective type has a problem of low consumption efficiency and a long drying time. Recently, many studies have been conducted on a drying method using microwaves due to high energy efficiency. In this paper, drying was performed using a microwave for drying PC pellets. Energy consumption and drying efficiency analyzed by set up an experimental apparatus of heat convective, microwave, and hybrid(heat convective + microwave) types. It was confirmed that energy consumption and drying efficiency were high when drying using microwaves, and it was confirmed that the hybrid method improved drying performance compared to the heat convective method. It is expected that the research results of this paper can be used as basic data for drying plastic pellets using microwave.

• 한국태양에너지학회:학술대회논문집
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• 2009.04a
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• pp.121-126
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• 2009

This study analyzed the standard school's energy usage and patterns as the zero-energy goal of primary school building, and proposed the energy reduction process of school building using energy analysis computing simulation tool. As a analysis simulation tool, Visual DOE 4.0 is used. For analysis of actual energy usage, selected primary school that is standard in the nation's energy consumption. Standard of the school's energy consumption analysis were devided into electric and gas energy. Input parameters of the simulation program based on the literature material and field survey material. after, but it was calibrated to comparison with the standard school's energy consumption. Finally, its energy usage analyzed by component and made the priority order of energy saving. Applied energy saving technologies are envelopment insulation, high efficiency lighting, high performance HAVC system and used active equipment system of solar collector and photovoltaic generation for additional savings.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.200-205
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• 2012

In the high oil price age, intensification of energy efficiency promotion in the building sector is required. Windows are dominating in large percent of whole building loads, and are regarding as the primary target of energy efficiency. In this study, in order to reduce heat loss of buildings, we investigate the thermal performance properties of Temperable Low-e glazing coated Ag membrane that has high electrical conductivity. The Temperable Low-e glazing windows has high insulation and shading properties, and it has strength that can supply various product which consumers want. In order to evaluate thermal performance of temperable windows, we install single low-e windows and double low-e windows in the experimental chamber and analysis the comparison heating energy consumption between single and double Low-e glazing windows. performance evaluation was conducted.

• The KIPS Transactions:PartA
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• v.14A no.1 s.105
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• pp.55-62
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• 2007

Uptime of embedded processors for mobile devices are dependent on battery consumption. Especially the large portion of power consumption is known to be due to cache management in embedded processors. This paper proposes an energy efficient data cache structure for high performance embedded processors. High performance prefetching data cache issues prefetching instructions before issuing demand-fetch instructions based on reference predictions. These prefetching instruction bring reduction on memory delay by improving cache hit ratio, but on the other hand those increase energy consumption in proportion to the number of prefetching instructions. In this paper, we adopt tag history table on prefetching data cache for reducing energy consumption by minimizing parallel tag comparison. Experimental results show the proposed data cache improves performance on energy consumption as well as memory delay.

• Journal of the Korean Solar Energy Society
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• v.33 no.1
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• pp.24-31
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• 2013

More than 23% of total nation's energy is consumed by residential building and 57.2% of Korean people are living in apartment. This study was carried out to two kind of process. First, after selecting one standard apartment, our research team investigate realistic energy consumption. Second, using 3-dimension heat transfer tool(TRISCO RADICON) and building energy simulation tool(Visual DOE) As a result, amount of heating and hot-water energy is composed of above 80 percent in standard apartment. And, after applying high performance technologies to standard apartment, namely, after being green home apartment, total energy consumption is reduced by54.6 percent. Also, because of energy consumption characteristics of green home apartment, for making more high performance green home apartment, especially, we have to figure out effective method to reduce electric and hot water energy.