• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 A
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• pp.58-60
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• 2000

In this paper we present an estimation method of electric energy[kWh] for load management of pole-transformer. For the electric energy estimation, we use the nonlinear load research based estimation(NLRE) algorithm. The NLRE curve is the normalized annual cumulative energy consumption for a particular day in a year. And, it is used for the coefficient estimation. Estimation method of suggested electric energy of pole-transformer used billing cycle electric energy estimation equation is verified as comparison billing cycle electric energy and estimated electric energy. We can reduce the error of peak load estimation by suggested method than the conventional method in domestic.

• 전기학회논문지
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• 제66권5호
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• pp.745-749
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• 2017

Climate change and energy security are major factors for future national energy policy. To resolve these issues, many countries are focusing on creating new growth industries and energy services such as smartgrid, renewable energy, microgrid, energy management system, and peer to peer energy trading. The financial and economic evaluation of new energy services basically requires energy savings estimation technologies. This paper presents the baseline load estimation method, which is used to calculate energy savings resulted from participating in the new energy program, using moving average model with heating degree days (HDD) and cooling degree days (CDD) adjustment. To demonstrate the improvement of baseline load estimation accuracy, the proposed method is tested. The results of case studies are presented to show the effectiveness of the proposed baseline load estimation method.

• 대한건축학회논문집:구조계
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• 제36권5호
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• pp.165-176
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• 2020

The purpose of this study is to develop a estimation method of energy consumption by end-use in office buildings. For this, the current status of information on building energy use was investigated, and the domestic and foreign literature on the classification of energy use in non-residential buildings and the estimation method of energy use were reviewed. In addition, the characteristics of energy consumption by end-use were analyzed with measurement data of 48 office buildings in Seoul. As results, the annual and monthly estimation method of energy consumption by end-use in office buildings using public and measurement data was presented, and the applicability of the estimation method was examined by applying to sample office buildings.

• Nuclear Engineering and Technology
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• 제53권10호
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• pp.3431-3437
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• 2021

In this study, one-dimensional gamma ray source positions are estimated using a plastic scintillating optical fiber, two photon counters and via data processing with a machine learning algorithm. A nonlinear regression algorithm is used to construct a machine learning model for the position estimation of radioactive sources. The position estimation results of radioactive sources using machine learning are compared with the theoretical position estimation results based on the same measured data. Various tests at the source positions are conducted to determine the improvement in the accuracy of source position estimation. In addition, an evaluation is performed to compare the change in accuracy when varying the number of training datasets. The proposed one-dimensional gamma ray source position estimation system with plastic scintillating fiber using machine learning algorithm can be used as radioactive leakage scanners at disposal sites.

• 한국환경과학회지
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• 제24권11호
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• pp.1443-1450
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• 2015

Wastewater treatment plant(WWTP) has been recognized as a high energy consuming plant. Usually many WWTPs has been operated in the excessive operation conditions in order to maintain stable wastewater treatment. The energy required at WWTPs consists of various subparts such as pumping, aeration, and office maintenance. For management of energy comes from process operation, it can be useful to operators to provide some information about energy variations according to the adjustment of operational variables. In this study, multiple regression analysis was used to establish an energy estimation model. The independent variables for estimation energy were selected among operational variables. The $R^2$ value in the regression analysis appeared 0.68, and performance of the electric power prediction model had less than ${\pm}5%$ error.

• 한국정보통신학회논문지
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• 제20권3호
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• pp.506-512
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• 2016

최근 에너지 하베스팅 기술은 배터리 용량 부족 문제를 해결하여 네트워크 수명을 향상시킬 수 있는 방안으로 관심을 받고 있다. 하지만 기존 연구의 경우 정확한 채널정보를 바탕으로 한 이상적인 환경에서의 하베스팅 기술만을 고려하였다. 본 논문에서는 채널 추정 절차와 이에 따른 채널 추정 오차를 반영한 현실적 에너지 하베스팅 네트워크 환경에서 에너지 효율성을 향상시키기 위한 자원 할당 기법을 제안한다. 제안 기법에서는 최적화 기법을 이용하여 시스템 데이터 전송률, 에너지 획득량, 불완전한 채널 추정 특성 등을 동시에 고려한 스케줄링 및 파워 할당 해를 찾는다. 제안 기법은 에너지 효율성 관점에서 기존의 하베스팅 기법보다 향상된 성능을 보이며, 채널 추정 오차가 반영되었을 때의 에너지 효율적 자원할당 방법에 대한 새로운 정보를 제공한다.

• 한국태양에너지학회:학술대회논문집
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• 한국태양에너지학회 2008년도 추계학술발표대회 논문집
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• pp.288-293
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• 2008

A predictive model of wind speed in the wind farm has very important meanings. This paper presents an estimation model of wind speed based on time series analysis using the observed wind data at Hangyeong Wind Farm in Jeju island, and verification of the predictive model. In case of Hangyeong Wind Farm and Haengwon Wind Farm, The ARIMA(Autoregressive Integrated Moving Average) predictive model was appropriate, and the wind speed estimation model was developed by means of parametric estimation using Maximum likelihood Estimation.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.723-727
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• 2015

Building energy use estimation relies on building characteristics, its energy systems, occupants, and weather. Energy estimation of new buildings is considerably an easy task when compared to modeling existing buildings as they require calibration with actual data. Particularly, when energy estimation of existing building stock is warranted at a city-scale, the problem is exacerbated owing to lack of construction drawings and other engineering specifications. However, as collection of buildings and other infrastructure constitute cities, such predictions are a necessary component of developing and maintaining sustainable cities. This paper uses Artificial Neural Network techniques to predict electricity consumption for residential buildings situated in the City of Gainesville, Florida. With the use of 32,813 samples of data vectors that comprise of building floor area, built year, number of stories, and range of monthly energy consumption, this paper extends the prediction to environmental impact assessment of electricity usage at the urban-scale. Among others, one of the applications of the proposed model discussed in this paper is the study of urban scale Life Cycle Assessment, and other decisions related to creating sustainable cities.

• KIEAE Journal
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• 제10권1호
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• pp.57-64
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• 2010

Electric energy is indispensible of the development of the industrial and living sector. Among the energy sectors, the building area shares 20% of the produced electric power in Korea. As we plan to supply the apartment, we need to forecast the required amount of the electric energy and supply the infrastructure to apartment for the lighting, cooling. Nonetheless, it is not easy to forecast the required amount of the electric energy, considering the management aspect, building physical aspect and social-geographic aspect. In this paper, it studied the estimation model of the electric energy, reflecting the affecting variables such as total area, number of household, geography and so on. The estimation model is proposed in 3-types which explained in central heating, individual heating and district heating, and each type have two estimation model, reflecting the affecting variable and corelation between variables to eliminate the muticolinearity. The unit of electric energy consumption per area and year is similar in three heating type and the results are as follows; the central heating is $34.446kWh/yr{\cdot}m^2$, individual type is $35.756446kWh/yr{\cdot}m^2$ and district heating is $34.285446kWh/yr{\cdot}m^2$.

• Nuclear Engineering and Technology
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• 제32권3호
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• pp.227-234
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• 2000

It is known that loose parts in the reactor coolant system (RCS) cause serious damage to the systems. This paper is concerned with estimating the mass of a loose part in the steam generator of a nuclear power plant. We developed the mass estimation algorithm based on the Hertz theory in order to estimate the mass of the loose parts and applied the algorithm to the impact test data of YGN3. The mass estimation values were compared with real values in order to verify the algorithm. The result showed that the average error of the mass estimation value is less than 27%.