• 한국태양에너지학회:학술대회논문집
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• 2011.04a
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• pp.127-132
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• 2011

Worldwide, the building energy simulation becomes inevitable step for predicting the energy consumption in building. In simulation process, the expertise is required for the accurate analysis results. In Korea, however, most of user use the inconsistent data with Korea circumstance. In this step, we need to construct the standard input data matched building in Korea. In this study, electricity consumption of apartments in Daejeon is analyzed. The yearly data of a apartment complexes of 2009 are analyzed as monthly, daily(week and weekend), timely, and completion year. With this result, we are able to predict the demand pattern of electricity in a house and make the schedule by demand pattern. The results of this study are followed. The averaged amount of electricity consumption in winter is higher than summer because of the high capacity of heating equipment. All of the house has electric base load from 0.26kWh to 0.5kWh. The average of the electricity consumption of month is shown as 326.7kWh. A week is seperated as 4 part such as week, weekend, Saturday and Sunday. During week, the average of timely electricity consumption is shown as 0.442kWh. The Saturday consumption is 0.453kWh. The Sunday is 0.461kWh.

• Journal of Energy Engineering
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• v.27 no.3
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• pp.10-20
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• 2018

Due to cheap electricity tariff on agricultural use, electricity consumption in agricultural sector has grown dramatically. We evaluated the negative effects of the cheap electricity tariff such as electricity over-consumption, increased dependency on electricity, decreased electricity productivity, and unequal distribution of the benefit. We also estimated the effects of agricultral output growth, structural change, and electricity intensity change on sharp electricity consumption increase in agricultural sector between 1998 and 2016 using the Log Mean Divisia Index decomposition method. The findings reinforce the necessity of raising the electricity tariff for agricultural use.

• Architectural research
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• v.21 no.1
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• pp.21-29
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• 2019

Recent years in Korea, some new developed buildings are only using electricity as power for heating, cooling, bathing and even cooking which means except electricity, there is no natural gas or other kinds of energy used in such kind of building. In vehicle industry area, scientists already invented electric vehicle as an environment friendly vehicle; after that, in architecture design and construction field, buildings only using electricity appeared; the curiosity of the environment impact of energy consumption by such kind of building lead me to do this research. In general, electricity is known as a clean energy resource reasoned by it is noncombustible energy resource; however, although there is no environmental pollution by using electricity, electricity generation procedure in power plant may cause huge amount of environment pollution; especially, electricity generation from combusting coal in power plant could emit enormous air pollutants to the air. In this research, the yearly amount of air pollution by energy using under traditional way in research target building that is using natural gas for heating, bathing and cooking and electricity for lighting, equipment and cooling is compared with yearly amount of air pollution by only using electricity as power in the building; result shows that building that only uses electricity emits much more air pollutants than uses electricity and natural gas together in the building. According to the amount of air pollutants comparison result between two different energy application types in the building, residential SOFC (Solid oxide fuel cell) is simulated to apply in this building for decreasing environment pollution of the building; furthermore, high load factor could lead high efficiency of SOFC, in the scenario of simulating applying SOFC in the building, SOFC is shared by two or three households in spring and autumn to increase efficiency of the SOFC. In sum, this research is trying to demonstrate electricity is a conditioned environment friendly energy resource; in the meanwhile, SOFC is simulated efficiently applying in the building only using electricity as power to decrease the large amount of air pollutants by energy using in the building. Energy consumption of the building is analyzed by calibrated commercial software Design Builder; the calibrated mathematical model of SOFC is referred from other researcher's study.

• Journal of the Korea Convergence Society
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• v.8 no.1
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• pp.19-23
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• 2017

Energy is a vital resource for the economic growth and the social development for any country. As the industry becomes more sophisticated and the economy more grows, the electricity demand is increasing. So forecasting electricity demand is an important for electricity suppliers. Forecasting electricity demand makes it possible to distribute electricity demand. As the market for Negawatt market began to grow in Korea from 2014, the prediction of electricity consumption demand becomes more important. Moreover, power consumption forecasting provides a way for demand management to be directly or indirectly participated by consumers in the electricity market. We use Genetic Algorithms to predict the energy demand of the fishing industry in Jeju Island by using GDP, per capita gross national income, value add, and domestic electricity consumption from 1999 to 2011. Genetic Algorithm is useful for finding optimal solutions in various fields. In this paper, genetic algorithm finds optimal parameters. The objective is to find the optimal value of the coefficients used to predict the electricity demand and to minimize the error rate between the predicted value and the actual power consumption values.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.669-675
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• 2012

Recently, the amount of electrical heat pump(EHP), a electrical conditioning equipment, is sharply increasing due to the luxury and multi-story trend of building. Accordingly, the cooling load that occupying substantial part of summer electric consumption has increased dramatically, making difficulties in domestic supply of electricity in summer. There are some efforts to replace it with an alternative cooling equipment such as gas heat pump(GHP), a gas cooling equipment, in order to solve the problem of summer electricity supply through reducing the summer electricity peak. It is rare, however, to find studies on the actual effects of GHP on the reduction of summer electricity peak. This study, therefore, estimated the effects of the GHP on the summer electricity peak by the gas consumption and characteristics of gas cooling systems. In addition, electric substitution effects by gas cooling systems were analyzed through case studies in the summer of 2010.

• Journal of Energy Engineering
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• v.24 no.1
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• pp.137-148
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• 2015

So far, the phenomenon of "electrification" has been deepened in Korean industry and especially direct heating energy which accounted for 44.0%(2010) of total energy consumed in Korean manufacturing has been significantly electrified. This paper decomposed electricity consumption for direct heating in Korean manufacturing from 1992 to 2012 using LMDI(Log Mean Divisia Index). This paper includes 4 different factors such as electricity proportion effect, direct heating proportion effect, energy intensity effect and added value effect. And this paper compared the consumption pattern by business type. As results, electricity proportion effect had contributed the most to the increase of electricity consumption for direct heating in Korean manufacturing. And Petrol-Chemical and Iron & Steel had the most electrification of direct heating.

• Journal of Energy Engineering
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• v.28 no.2
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• pp.1-17
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• 2019

This article analyzes causal relationships among gross domestic product(GDP), electricity consumption, carbon dioxide($CO_2$) emission and foreign direct investments(FDI) inflow of Korea over the period from 1976 to 2014, using unit root test, cointegration test, and vector error correction model(VECM). As the results, this article found (1) a long-run bi-directional causality between GDP and electricity consumption, which may imply a negative impact of electricity consumption-saving policy on economic growth, (2) uni-directional short- and long-run causalities running from $CO_2$ emission to GDP, and a uni-directional long-run causality running from $CO_2$ emission to electricity consumption, which can result in a negative impact of $CO_2$ emission reduction policy on economic growth and electricity consumption, (3) a uni-directional long-run causality running from FDI to GDP, and uni-directional short- and long-run causalities running from FDI to electricity consumption, which may result from relatively lower electricity prices than investing countries, (4) no causality between FDI and $CO_2$ emission, which is based on the characteristics of FDI composed of service industries. Considering the above causal relationships among the four variables, the policy implication needs to focus on the electricity demand management based on the relevant R&Ds, and on the gradual transition from fossil fuel- to renewable-energy. Adaptive policy to increase the FDI inflow is also needed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.10
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• pp.4506-4513
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• 2012

The purpose of this study is to analyze the causality of electricity consumption and econmic growth and draw policy implications. To do this, we used Testing Prodedures of Unit Root and Cointegration and then VECM and Granger causality test using data taken from China over the period 1971 to 2008. As results, there are long and short term causalities between electricity consumption and economic growth of China. These results provide a few implications to policy analysts in China. First it is still available that the electricity comes before the economic development. The increase of electricity consumption promotes economic growth. Of course there are other factors to the economic growth, but the stable supply of electricity is necessary. Second, this paper confirms the assertion that the increase of GDP expands electric consumption is valid.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.162-175
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• 2018

Currently, electricity consumption and feedback mechanisms are being widely researched in Internet of Things (IoT) areas to realise power consumption monitoring and management through the remote control of appliances. This paper aims to develop a smart electricity utilisation IoT platform with a deep belief network for electricity utilisation feature modelling. In the end node of electricity utilisation, a smart monitoring and control module is developed for automatically operating air conditioners with a gateway, which connects and controls the appliances through an embedded ZigBee solution. To collect electricity consumption data, a programmable smart IoT gateway is developed to connect an IoT cloud server of smart electricity utilisation via the Internet and report the operational parameters and working states. The cloud platform manages the behaviour planning functions of the energy-saving strategies based on the power consumption features analysed by a deep belief network algorithm, which enables the automatic classification of the electricity utilisation situation. Besides increasing the user's comfort and improving the user's experience, the established feature models provide reliable information and effective control suggestions for power reduction by refining the air conditioner operation habits of each house. In addition, several data visualisation technologies are utilised to present the power consumption datasets intuitively.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.189-204
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• 2018

This study is on forecasting the electricity consumption of an industrial manufacturing building called GGM from January 2014 to April 2017. We fitted models using SARIMA, SARIMA + GARCH, Holt-Winters method and ARIMA with Fourier transformation. We also forecasted electricity consumption for one month ahead and compared the predicted root mean square error as well as the predicted error rate of each model. The electricity consumption of GGM fluctuates weekly and annually; therefore, SARIMA + GARCH model considering both volatility and seasonality, shows the best fit and prediction.