• 한국농공학회논문집
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• 제50권4호
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• pp.99-109
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• 2008

As the price of traditional fossil fuels continue to increase, more people attach importance to the pollution of the environment caused by fossil fuel's burning, developing and using renewable energy resources has become a very important project all over the world. Also, the rural energy planning which is another method to improve energy utilization ratio and reduce environment pollution, is also regarded as a very effective way to reduce the energy consumption. There is a quantity of renewable energy resources and natural tribes in rural area, which is both feasible to develop the renewable energy and the regional energy planning. To carry out this, it is needs to know the area's quantity of renewable energy resources and the total energy consumption. This paper is to find out the relationship between rural energy consumption and rural conditions, and to found a energy consumption model which can conjecture the energy consumption in rural family. and the cost of rural family's energy consumption was founded to conjecture how much money dose it cost in rural family's energy consumption. The energy consumption model was concluded using the surveys of 76 families in 14 villages at the area of Chungcheongbuk-Do(province). The main factors to energy consumption was selected out which were number of family members, acreage of house, acreage of farmland and family's annual income.

• International Journal of Computer Science & Network Security
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• 제22권7호
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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.

• 신재생에너지
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• 제6권3호
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• pp.13-21
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• 2010

This paper analyzes the factors of renewable energy consumption in Korea, China and Japan. We consider renewable energy consumption per capita as dependent variable, GDP per capita, $CO_2$ emissions per capita and real oil prices as independent variables. To analyze this model, this paper uses three econometric methods such as OLS, fixed effect model and panel GLS, utilizing data from 1990 to 2006 in Korea, China and Japan. According to the results by OLS for each country, an increase in GDP per capita or $CO_2$ emissions per capita or oil prices leads to an increase in renewable energy consumption. According to the results by fixed effect model, an increase in GDP per capita or $CO_2$ emissions per capita leads to an increase in renewable energy consumption. And real oil prices do not have a significant impacts on this model. According to the results by panel GLS, an increase in real GDP per capita as a proxy of income leads to an increase renewable energy consumption. And both $CO_2$ emissions per capita and real oil prices do not correlated closely with renewable energy consumption. Thus oil is not substituted to renewable energy in Northeast asian countries.

• 한국산학기술학회논문지
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• 제16권10호
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• pp.6799-6806
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• 2015

최근 물류센터는 대형화 첨단화에 따른 다양한 설비 및 장비의 도입으로 전기에너지 소비가 급격히 증가하고 있다. 본 연구는 물류센터의 전기에너지 사용 현황 및 소비 특성을 정량적으로 분석하고, 효율을 평가하기 위한 전기에너지 표준소비량을 추정하는 모형을 구축하는 것을 목적으로 한다. 제시된 모형은 물류센터의 온도요인이 전기에너지 소비에 큰 영향을 미치는 특성을 효과적으로 반영하기 위하여 열역학 이론을 도입하였다. 모형은 물류센터 벽면의 열전도, 출입문 열대류 및 취급물품의 열 손실로 구성된 냉동기 운용에너지 부문과 물류활동을 위한 기계설비의 전력소모 부문으로 구성된다. 모형은 또한 물류센터 운영자가 에너지 소비 효율을 평가하고 개선전략을 수립하는 것을 지원할 수 있도록 다양한 설명변수들을 포함한다. 실제 물류센터의 에너지 소비량을 기반으로 본 연구에서 개발된 모형의 적용성이 평가된다.

• 에너지공학
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• 제22권1호
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• pp.38-43
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• 2013

생활양식의 변화나 생활행위의 변화에 관련한 가정부문 에너지소비 패턴의 분석을 위해서 용도별 에너지소비자료 작성이 절실히 요청되어 왔다. 이에 따라, 생활행위의 분류에 의한 가정부문 용도별 에너지소비 분석모형을 개발하여 에너지소비 분석을 시도하였다. 본고에서는 부위(방)별 에너지 사용기기의 에너지 소비효율에 생활행위별 기기마다 가동시간을 곱해서 가구당 에너지소비량이 산출될 수 있도록 상향식의 가정 내 모형을 구축하였다. 생활행위 분류를 감안한 용도는 조명용, 난방용, 냉방용, 오락용, 정보용, 위생용, 취사용 등으로 구분하였다.

• 한국통신학회논문지
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• 제32권6B호
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• pp.316-324
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• 2007

In this paper we proposed an energy consumption model for IR-UWB wireless sensor networks. The model takes the advantages of PHY-MAC cross layer design, and we used slotted and un-slotted sleeping protocols to compare the energy consumption. We addressed different system design issues that are responsible to energy consumption and proposed an optimum model for the system design. We expect the slotted sleeping will consume less energy for bursty load than that of the un-slotted one. But if we consider latency, the un-slotted sleeping model performs better than the slotted sleeping case.

• 산업경영시스템학회지
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• 제42권1호
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• pp.129-136
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• 2019

In this study, we proposed a model for forecasting power energy demand by investigating how outside temperature at a given time affected power consumption and. To this end, we analyzed the time series of power consumption in terms of the power spectrum and found the periodicities of one day and one week. With these periodicities, we investigated two time series of temperature and power consumption, and found, for a given hour, an approximate linear relation between temperature and power consumption. We adopted an exponential smoothing model to examine the effect of the linearity in forecasting the power demand. In particular, we adjusted the exponential smoothing model by using the variation of power consumption due to temperature change. In this way, the proposed model became a mixture of a time series model and a regression model. We demonstrated that the adjusted model outperformed the exponential smoothing model alone in terms of the mean relative percentage error and the root mean square error in the range of 3%~8% and 4kWh~27kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric energy together with the outside temperature.

• 설비공학논문집
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• 제26권8호
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• pp.394-400
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• 2014

Several models and methods have been developed to verify the improvement of energy performance in retrofit buildings. The verification is important to confirm the effectiveness of new technologies or retrofits. Inverse model toolkit proposed by ASHRAE evaluates the changes of the energy performance of retrofit buildings by using actual energy consumption data. In this study, the inverse model toolkit was used to analyze heating and cooling energy performance of an office building. Analyzed coefficients of correlation of actual energy consumption with estimated energy consumption was above 0.92 and well fitted. It was confirmed that energy consumption of natural gas decreased by 43.4% and also that electricity decreased by 13.8%, after the retrofit of the case building. For the energy usage, cooling energy was increased by 7.4%, heating energy was decreased by 42.3%, hot water and cooking were increased by 3.4%, lighting and electronics were decreased by 19.3%, and the total energy was decreased by 18.9%.

• 설비공학논문집
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• 제15권4호
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• pp.287-293
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• 2003

This study examined the temperature-dependent regression model of energy consumption based on various measuring period. The methodology employed was to construct temperature-dependent linear regression model of daily energy consumption from one day to three months data-sets and to compare the annual heating energy consumption predicted by these models with actual annual heating energy consumption. Heating energy consumption from a building in Daejon was examined experimentally. From the results, predicted value based on one day experimental data can have error over 100%. But predicted value based on one week experimental data showed error over 30%. And predicted value based on over three months experimental data provides accurate prediction within 6% but it will be required very expensive.

• 아태비즈니스연구
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• 제14권4호
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• pp.305-318
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• 2023

Purpose - This study analyzed the decoupling phenomenon between energy consumption and economic growth in Korea from 1990 to 2021. The main purpose of this study is to suggest policy implications for achieving a low-carbon society and decoupling that Korea must move forward in the face of the climate change crisis. Design/methodology/approach - This study investigated the relationship between energy consumption and economic growth by energy source and sector using the energy-EKC (EEKC) hypothesis which included the energy consumption on the traditional Environmental Kuznets Curve (EKC), and the impulse response function (IRF) model based on Bayesian vector auto-regression (BVAR). Findings - During the analysis period, the trend of decoupling of energy consumption and economic growth in Korea is confirmed starting from 1996. However, the decoupling tendency appeared differently depending on the differences in energy consumption by sources and fields. The results of the IRF model using data on energy consumption by source showed that the impact of GDP and renewable energy consumption resulted in an increase in energy consumption of bio and waste, but a decrease in energy consumption by sources, and the impact of trade dependence was found to increase the consumption of petroleum products. Research implications or Originality - According to the main results, efficient distribution by existing energy source is required through expansion of development of not only renewable energy but also alternative energy. Additionally, in order to increase the effectiveness of existing energy policies to achieve carbon neutrality, more detailed strategies by source and sector of energy consumption are needed.