• Journal of Korean Society for Quality Management
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• v.49 no.3
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• pp.327-340
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• 2021

Purpose: The purpose of this study is to propose a system predicting whether an electricity distribution system is abnormal by analyzing the temperature of the deteriorated system. Traditional electricity distribution system abnormality diagnosis was mainly limited to post-inspection. This research presents a remote monitoring system for detecting thermal images of the deteriorated electricity distribution system efficiently hereby providing safe and efficient abnormal diagnosis to electricians. Methods: In this study, an object detection algorithm (YOLOv5) is performed using 16,866 thermal images of electricity distribution systems provided by KEPCO(Korea Electric Power Corporation). Abnormality/Normality of the extracted system images from the algorithm are classified via the limit temperature. Each classification model, Random Forest, Support Vector Machine, XGBOOST is performed to explore 463,053 temperature datasets. The process capability index is employed to indicate the quality of the electricity distribution system. Results: This research performs case study with transformers representing the electricity distribution systems. The case study shows the following states: accuracy 100%, precision 100%, recall 100%, F1-score 100%. Also the case study shows the process capability index of the transformers with the following states: steady state 99.47%, caution state 0.16%, and risk state 0.37%. Conclusion: The sum of caution and risk state is 0.53%, which is higher than the actual failure rate. Also most transformer abnormalities can be detected through this monitoring system.

• Proceedings of the Korean Nuclear Society Conference
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• 2001.05a
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• pp.523.2-523
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• 2001

• Journal of Energy Engineering
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• v.24 no.2
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• pp.59-71
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• 2015

This study attempts to examine the economic impacts of electricity industry in Korea and Japan using an inter-industry analysis. Specifically, the study analyzes and compares electricity industry between Japan and Korea through production-inducing effect and value added inducing effect of electricity industry based on demand-driven model. Moreover, this study deals with supply shortage effect and sectoral price effect by using supply-driven model and Leontief price model, respectively. This study analyses the electricity industry through exogenous approach. The results show that electricity industry induces prodution-inducing effect of 0.5946 won in other industries in Korea and 0.5446 yen in other industries in Japan. Value-added-inducing effects are 0.1716 won in other in other industries in Korea and 0.2929 yen in other industries in Japan. Supply shortage effects of electricity industry are 1.5932 won in other industries in Korea and 1.2801 yen in other industries in Japan. And sectoral price effects are 0.2113% in Korea and 0.2196% in Japan due to the price increase of 10% of electricity industry.

• Journal of Energy Engineering
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• v.24 no.2
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• pp.167-173
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• 2015

This paper attempts to estimate the electricity demand function in Korea with quarterly data of average temperature, GDP and electricity price over the period 2005-2013. We apply lagged dependent variable model and ordinary least square method as a robust approach to estimating the parameters of the electricity demand function. The results show that short-run price and income elasticities of the electricity demand are estimated to be -0.569 and 0.631, respectively. They are statistically significant at the 1% level. Moreover, long-run income and price elasticities are estimated to be 1.589 and -1.433, respectively Both of results reveal that the demand for electricity is price- and income-elastic in the long-run. The relationship between electricity consumption and temperature is supported by many of references as a U-shaped relationship, and the base temperature of electricity demand is about $15.2^{\circ}C$. It is shown that power of explanation and goodness-of-fit statistics are improved in the use of the lagged dependent variable model rather than conventional model.

• Environmental and Resource Economics Review
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• v.28 no.3
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• pp.385-413
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• 2019

This study examines the determinants of net export of electricity among 30 European countries sharing electricity grid during the period of 1990~2014 by separating the sample period before and after 2005 in which ETS was introduced in Europe. The empirical method used in this study is generalize least squared one considering both heterogeneous and serial correlation in the balanced panel data. According to the empirical results, after 2005 introducing the ETS, holing energy resources, concentrating only on few electricity generation resources, and nuclear electricity generation had played more important role in net export of electricity, while renewable energy had negative effect on net export of electricity and coal and gas generation have no effect on net export after introduction of ETS in Europe probably because of high environmental cost. The policy implication of the results would be that reconsidering each country's optimal generation mix strategy and its role in case freely trading electricity.

• Environmental and Resource Economics Review
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• v.28 no.1
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• pp.147-176
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• 2019

This paper uses research on the Levelized Cost of Electricity (LCOE) in South Korea to conduct a simulation analysis on the impact of nuclear power dependency and usage rates on the social costs of power generation. We compare the $7^{th}$ basic plan for long-term electricity supply and demand, which was designed to increase nuclear power generation, to the $8^{th}$ basic plan for long-term electricity supply and demand that decreased nuclear power generation and increased renewable energy generation in order to estimate changes in social costs and electricity rates according to the power generation mix. Our environmental generation mix simulation results indicate that social costs may increase by 22% within 10 years while direct generation cost and electricity rates based on generation and other production costs may increase by as much as 22% and 18%, respectively. Thus we confirm that the power generation mix from the $8^{th}$ basic plan for long-term electricity supply and demand compared to the $7^{th}$ plan increases social costs of generation, which include environmental external costs.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.81-91
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• 2024

Electrical energy is a key source of energy for modern civilization, and changes in electricity generation and consumption are closely related to industry and life in general. In this study, we identified the correlation between electricity generation and nighttime light values in South Korea and used it to predict monthly electricity generation trends in North Korea. The results of the study showed a low Pearson correlation coefficient of 0.34 between nighttime light and electricity generation in Seoul, but a high Pearson correlation coefficient of 0.79 between weighting for Seoul case nighttime light values and electricity generation using monthly average temperature. Using nighttime light values weighting for Seoul case by the average monthly temperature in Pyongyang to predict the monthly power generation trend in North Korea, we found that the month-on-month power generation increase in December 2022 was about 60% higher than the month-on-month power generation increase in December 2020 and 2021. The results of this study are expected to help predict monthly electricity generation trends in regions where monthly electricity generation data does not exist, making it difficult to identify timely industry trends.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• KIEAE Journal
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• v.15 no.6
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• pp.5-10
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• 2015

Purpose: To evaluate the economic performance of grid-connected photovoltaic system in residential house, household electricity bill policy of Korea Electric Power Corporation (KEPCO) must be applied precisely, and market tendency and uncertainty of system also need to be considered. In this study, to evaluate the economic feasibility of PV system, we measured PV power generation and electricity consumption of six of Green home in Daejeon through web based remote monitoring system. Method: We applied Monte-Carlo simulation based on life cycle cost analysis, to reflect an uncertainty of main factor in economic feasibility evaluation of photovoltaic system. Result: First, with deterministic analysis, the difference of NPV of cumulative financial savings among households varied from -3,310 ~ 24,170 thousand won, portraying notably big range. Also the possibility of getting the same result was 50% when applying uncertainty. Second, the higher electricity consumption is, the more economic feasibility of photovoltaic system increases because KEPCO uses progressive taxation in household electricity bill policy. Third, The contribution to variance of electricity price increases in NPV varied from 98.5% to 99.9%. While the inflation rate and annual degradation contributed very little to none.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1601-1607
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• 2016

Recently, weather information has been increasingly used in various area. This study presents the necessity of hourly weather information for electricity demand forecasting through correlation analysis and multivariate regression model. Hourly weather data were collected by Meteorological Administration. Using electricity demand data, we considered TBATS exponential smoothing model with a sliding window method in order to forecast electricity demand. In this paper, we have shown that the incorporation of weather infromation into electrocity demand models can significantly enhance a forecasting capability.