• 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.

• 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.

• 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.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.506-510
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• 2012

This study suggested an improved algorithm of urban energy consumption estimation on the urban planning stage which concerns calculation accuracy. The results are as follows. (1) Urban energy consumption was estimated and managed per unit space using E-GIS DB which contains facility information per mesh. (2) Urban energy consumption was reflected by the urban facility classified and standardized by the characteristics of energy use. (3) Calculation accuracy of energy consumption was approached by separately suggested as summer algorithm reflecting urban heat island on summer energy use and winter algorithm reflecting heating system normally used in Korea.

• Journal of the Korean Solar Energy Society
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• v.39 no.5
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• pp.29-40
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• 2019

In response to the rapid climate change, in order to save energy in the field of buildings, the country is planning not only zero energy buildings but also zero energy cities. In the Urban Development Project, the Energy Use Plan Report is prepared and submitted by predicting the amount of energy demand at the planning stage. However, due to the activation of zero-energy buildings and the increase in the supply of new and renewable energy facilities, the energy consumption behavior of buildings in the city is changing from the previous ones. In this study, to estimate urban energy demand of Zero Energy City, building energy demand forecasts based on "Passive plans for use of energy based primary energy consumption", "Actual building energy usage data from Korea Appraisal Board" and "data from Certification of Building Energy Efficiency Rating" as well as demand forecast according to existing "Consultation about Energy Use Plan Code" were calculated and then applied to Multifunctional Administrative City 5-1 zone to compare urban total energy demand forecasts.

• Journal of Environmental Policy
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• v.13 no.2
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• pp.99-129
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• 2014

This research estimates the impact of population aging on energy use and carbon emissions by energy sources and by industrial sectors in Korea until 2035. For the estimation, the structural change in household consumption expenditure identified by the age-specific consumption pattern was analyzed in conjunction with energy and environment input-output tables. The estimation result presents that, despite the population aging, energy use and carbon emissions induced by household consumption continue to increase until 2026, and then that elevated levels of energy use and carbon emissions will be maintained for a considerable period of time. According to the estimation by energy sources, the use of natural gas will show substantial increase while the use of crude oil will switch to a downturn at a relatively early period. According to the estimation by industrial sectors, carbon emissions in the sectors with relatively high consumption share of old households such as medical health, dwelling, lighting, heating, air-conditioning, and food will have substantial increase, whereas those in the sectors associated with education, transport, catering, and accommodation services will turn downward relatively early. In addition, the study analyzes through policy simulation the impact of aging-related policy similar to the basic pension system, which is recently being discussed for legislation, on energy use and carbon emissions.

• KIEAE Journal
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• v.9 no.1
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• pp.107-113
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• 2009

The energy cost is resulted from the energy use. Its sources are divided into some types and depended on the building use or energy-use type. The energy cost should be affected by the amount of the energy use. The cost could be calculated to consider various factors such as the insulation, heating type, building shape and others. But it can not consider all of the affect factors to the energy cost and need to categorize the factors to the condition for estimating the cost. In this paper, it aimed at providing the estimation model in linear equation and multiple linear regression, utilizing the building exterior condition and management characteristics in apartment housing. Its survey are conducted in two parts of management characteristics and building exterior condition. The correlation analysis is conducted to get rid of the multicolinearity among the inputted factors. The number of linear equation model is 11 and includes the 1st, 2nd and 3rd equation function, power function and others. Among these, it suggested the 2nd and 3rd function and power function in terms of the statistics. In multiple linear regression model, the building volume and management area are inputted to the estimation.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.10
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• pp.454-463
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• 2001

This paper presents a method for real-time estimation of TCSC reference quantity in order to enhance the power system transient stability energy margin using artificial neural network in multi-machine system. This paper has the three parts, the first part is to determine the lines to be installed by TCSC. The seconds is to estimate the energy margin using by ANN. To get the critical energy for training, we use the potential energy boundary surface(PEBS) method which is one of the transient energy function(TEF) method. And the last is to determine the TCSC reference quantity. In order to make training data for ANN in this step, we use genetic algorithm(GA). The proposed method is applied to 39-bus, 46-line. 10-machine model system to show its effectiveness.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.242-244
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• 2000

This paper presents a method for real-time estimation of TCSC quantity in order to enhance the power system transient stability energy margin using fuzzy neural network in multi-machine system. This paper has two parts, the first part is to estimate the energy margin. To set critical energy, we use the potential energy boundary surface(PEBS) method which one of the transient energy function(TEF) method. And the second is to determine the TCSC quantify and the line to be injected. In order to make training data in this step, we use genetic algorithm. The proposed method is applied to 6-bus, 7-line, 4-machine model system to show its effectiveness.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1990.10a
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• pp.28-34
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• 1990

Lighting is one of the largest energy, integrated lighting system with daylight and artificial lighting has been suggested. In such system, perimeter zone can be illuminated by daylighting and the deep area of room by artificial lighting. So, the study is to develope estimation nomograph of lighting energy by turnning-off depth and lighting control systems during daytime. For the purpose, energy nomograph has been developed to apply to side-lit office guilding and the use and limitation of the nomograph has been discussed.