• Proceedings of the KIEE Conference
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• 2000.11a
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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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.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.5
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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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• v.53 no.10
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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.

• Journal of Environmental Science International
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• v.24 no.11
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.506-512
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• 2016

Recently, energy harvesting technology is considered as a tool to improve the lifetime of sensor networks by mitigating the battery capacity limitation problem. However, the previous work on energy harvesting has failed to provide practical information since it has assumed an ideal channel knowledge model with perfect channel state information at transmitter (CSIT). This paper proposes an energy efficient resource allocation scheme that takes account of the channel estimation process and the corresponding estimation error. Based on the optimization tools, we provide information on efficient scheduling and power allocation as the functions of channel estimation accuracy, harvested energy, and data rate. The simulation results confirm that the proposed scheme outperforms the conventional energy harvesting networks without considering channel estimation error in terms of energy efficiency. Furthermore, with taking account of channel estimation error, the results provides a new way for allocating resources and scheduling devices.

• International conference on construction engineering and project management
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• 2015.10a
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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.

• 한국태양에너지학회:학술대회논문집
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• 2008.11a
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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.

• KIEAE Journal
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• v.10 no.1
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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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• v.32 no.3
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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%.