• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.1
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• pp.37-46
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• 2019

The energy of the building is influenced by the user 's activity due to the population, society, and economic characteristics of the building user. In order to obtain accurate energy information, the difference in the amount of energy consumption by the activities and characteristics of building users should be identified. The purpose of the study is to identify the difference in the amount of energy consumption by the user's activities in the same building, and to analyse the relationship between user's activities and demographic, social and economic characteristics. For research, energy simulation is performed based on actual user activity schedule. The results of the simulation were clustered by using K-Means clustering, a machine learning technique. As a result, four types of users were derived based on the amount of energy consumption. The more energy used in a cluster, the lower the user's income level and older. The longer a user's indoor activity times, the higher the energy use, and these activities relate to the user's characteristics. There is more than twice the difference between the group that uses the least energy consumption and the group that uses the most energy consumption.

• Journal of Family Resource Management and Policy Review
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• v.14 no.3
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• pp.53-68
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• 2010

This study tried to develop the theoretical backgrounds, explaining consumers energy consumption behavior and analyzed its effects. In other words, this study investigated the factors influencing the amount of electronic energy consumption. In this study, we used 678 questionnaires which were selected a quota sampling by living area who were above 20 years old and married. Summary of results of this study follows. First, attitude for energy saving was positively related with female, high school graduated large size of family members, elderly, and middle-class consumers. In addition, consumers' search for energy saving were appeared passively in young consumers under 30 years old, and the family with the highest household income. Consumers' purchasing energy-efficient products was presented in large size of family members, and young consumers. Second, consumers' environmental oriented behavior, action-directed behavior, healthseeking behavior were significantly related with energy saving behavior, and active information search for energy saving, but not with purchasing energy-efficient products and consuming behavior of electrical energy. Third, the quantity of electric energy consumption was affected by the size of family members, the living size of house related with high energy demand, the attitude for energy saving, and searching information for energy-saving.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2781-2798
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• 2019

Thanks to internet, as one of indispensable parts of our lives, many devices that we use in our daily lives like TV, air conditioner, refrigerator, washing machine, can be monitored and controlled remotely by becoming more intelligent via Internet of Things (IoT) technology. Smart Home applications as one of the elements of smart cities, are individually the most demanded application without question. In this study, Smart Energy Management (SEM) system, based on NodeMCU and Android, has been designed for SEM, which is a part of the smart home application. With this system, household energy consumption can be monitored in real time, as well as having the ability to record the data comprising of operation times and energy consumption information for each device. Additionally, it is ensured to meet the energy needs on a maximized level possible, during the hours when the energy costs are lower owing to the SEM system. The Android interface provides the users with the opportunity to monitor and change their electricity consumption habits in order to optimize the energy efficiency, along with the opportunity to draw up of a daily and weekly schedule.

• Journal of Plant Biotechnology
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• v.41 no.3
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• pp.123-126
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• 2014

This paper is suitable for household plant factory by design and using both energy-saving LED and solar technology. Conventional household plant factory only depending on natural sunlight is sensitive for the change of external environment. Another a big problem of conventional common household plant factory is large power consumption. Recently interest in wellbeing food such as chemical-free is increased abruptly. To solve these two problems, this paper describes hybrid type of household plant. In particular, reducing the power photosynthesis photon flux density (PPFD) is kept uniform to enhance the growth of the plant. Ambient light sensor is adopted for the control of proper combination of sunlight and LED to keep PPFD constant.

• Journal of the Korean Society of School Health
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• v.31 no.3
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• pp.196-202
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• 2018

Purpose: The purpose of this study was to analyze the relationship between physical activity and energy drinks consumption in Korean Adolescents. Methods: This study was a secondary data analysis using statistics from the 2017(13th) Korea Youth Risk Behavior Web-based Survey. The variables used in the study were physical activity, energy drinks consumption and socio-demographic characteristics such as gender, academic achievement, household economic status and weekly allowance. The data were analyzed by $x^2$ test and multinominal logistic regression and the results were presented in percentage. Results: As the number of days engaging in moderate and vigorous physical activities increased, the response that they consume energy drinks 'more than 5 times a week' also increased. The subjects who participated in a 'moderate' or 'high' level of moderate physical activity consumed 1.4 times more energy drinks than those who do not participate in physical activity. And the subjects who engaged in a 'low' or 'moderate' level of vigorous physical activity consumed about 1.3 times more energy drinks than those who don't work out. Conclusion: As the level and intensity of physical activity increased, the number and frequency of energy drinks consumption increased. The results of this study can be used as basic data for intervention programs to reduce energy drinks consumption and promote proper physical activity.

• The Journal of Bigdata
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• v.6 no.1
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• pp.127-132
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• 2021

Korea is both a resource-poor country and a energy-consuming country. In addition, the use and dependence on electricity is very high, and more than 20% of total energy use is consumed in buildings. As research on deep learning and machine learning is active, research is underway to apply various algorithms to energy efficiency fields, and the introduction of building energy management systems (BEMS) for efficient energy management is increasing. In this paper, we constructed a database based on energy usage by device per household directly collected using smart plugs. We also implement algorithms that effectively analyze and predict the data collected using RNN and LSTM models. In the future, this data can be applied to analysis of power consumption patterns beyond prediction of energy consumption. This can help improve energy efficiency and is expected to help manage effective power usage through prediction of future data.

• KIEAE Journal
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• v.8 no.4
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• pp.19-27
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• 2008

The carbon dioxide is brought from the energy consumption and regarded as a criteria material to estimate the Global Warming Potential. Building shares about 30% in national energy consumption and affects to environment as much as the energy consumption. But there is not enough data to forecast the amount of the carbon dioxide during the maintenance stage. Various factors are related with the energy consumption and carbon dioxide emission such as the physical area, the building exterior area, the maintenance type and location. Among these factors, the building carbon-dioxide emission can be estimated by the overall building characteristics such as the maintenance area, the number of household, the heating type, etc., The physical amount such as the thickness of the insulation and window infiltration could explained the limited scope and might not be use to estimate the total carbon-dioxide emission energy because the each value could not include or represent the overall building. In this paper, it provided the estimation model of the carbon-dioxide emission, explained by the overall building characteristics. These factors are shown as the maintenance area, no. of household, the heating type, the volume of the building, the ratio of the window to wall area etc., For providing the estimation model of th carbon-dioxide emission, it conducted the corelation analysis to filter the variables and suggested the estimation model with the power model and multiple regression model. Most of the model have a good statistics and fitted in the curve line.

• Environmental and Resource Economics Review
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• v.19 no.1
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• pp.129-157
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• 2010

This paper is intended to evaluate energy efficiency policy in demand side, to assess the residential sector's energy efficiency policy and to analyze the system of energy efficiency practices. We examined residential energy consumption over the period 1990~2006. The decomposition method in the analysis was a logarithmic mean Divisia index (LMDI) techniques to decompose changes in energy intensity. First of all, the energy use in residential sector was adjusted to correct weather-induced variations in energy consumption, because adjustments for normal weather patterns facilitated inter-temporal comparison of intensity. The analysis on the residential sector shows that the overall energy intensity of the residential sector declined at an average 1.0% per year, while the structure effect increased by 1.8% per year, and the activity effect increased by 0.7% per year. In other words, the decline of floor space, number of household, and appliance ownership per capita has an effect on increase in residential consumption. The improvement in energy efficiency had strong contribution on the decrease of energy consumption. We find that the general results of analysis on residential energy are similar to those of IEA. The energy efficiency policy in residential sector is assessed to obtain some results during 1990~2006. In residential sector, structural variables such population per household, diffusion of appliance and activity factor such as population contributed to the increase of energy consumption while energy intensity effect induced the decrease of energy consumption. These findings are consistent with international trend as well as our prior expectation.

• KIEAE Journal
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• v.13 no.6
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• pp.55-65
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• 2013

Korean Government has developed Sejong City as a new administration city. This city of future was planned and designed toward one of the most eco-friendly city on the basis of ICTs. To attain this object, a carbon emission monitoring system (CEMS) was designed and installed as a part of u-city service which provides various information anytime and anywhere to enrich the people's quality of life. In this paper, at first, the structure and functions of CEMS are introduced. This system is consist of 5 parts - data collection from user and linked public DBs, transforming data into meaningful information for the policy makers, system-user interfacing via statistical tables and graphs, and system maintenance. This system can be operated by the citizen participation through whole the process. With the help of GIS map and graphic interface, statistics of monitored data for both citizen and decision maker provided and after feed-back, they have affected on the behaviour of citizen's energy consumption and related policy as well. By the CEMS, energy consumption data of 124 agreed households were collected during 9 months in 2012. Electricity, gas and water consumption were remote-metered automatically by the system and analysed. This showed that more than 85% of CO2 emission is rely on electricity usage. Furthermore, number of family members and size of house influences on the emission of CO2 by each household together with the life-style of the occupants. Electricity and water consumption showed the seasonal factor while gas consumption represents the number of family members. Even this paper has limitations caused by 9 months of data collection, it shows the policy directions to reduce the emission of CO2 focusing on the house size and number of family members of each households. With the result of this research, life-style of the generation of dwellers should be investigated and the CO2 emission characteristics of other housing type as well for the data building for future policy making.

• Journal of Power System Engineering
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• v.21 no.5
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• pp.5-12
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• 2017

With the difficulties in increasing the efficiency of conventional crank-driven compressors due to mechanical loss, compressor manufacturers have investigated new kinds of compressor such as a free piston compressor mechanism. This study investigates the energy efficiency of two different types of compressor for a household refrigerator. One is the conventional crank-driven compressor, and the other one is a linear compressor. The energy efficiencies of these compressors are evaluated. Experimental results show that the linear compressor has 10% lower power consumption than the brushless direct-current (BLDC) reciprocating compressor. The linear compressor demonstrates excellent energy efficiency by reducing the friction loss. Furthermore, a motor efficiency exceeding 90% is achieved by using a linear oscillating mechanism with a moving magnet. Additionally, the compressor stroke to piston diameter ratio of the oscillating piston in the linear compressor can be adjusted in order to modulate the cooling capacity of the compressor for improved system efficiency.