• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.35-42
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• 2021

In order to produce electric vehicle demand forecasting information, which is an important element of the plan to expand charging facilities for electric vehicles, a model for predicting electric vehicle demand was proposed using Exponential Smoothing. In order to establish input data for the model, the monthly power demand of cities and counties was applied as independent variables, monthly electric vehicle charging stations, monthly electric vehicle charging stations, and monthly electric vehicle registration data. To verify the accuracy of the electric vehicle power demand prediction model, we compare the results of the statistical methods Exponential Smoothing (ETS) and ARIMA models with error rates of 12% and 21%, confirming that the ETS presented in this paper is 9% more accurate as electric vehicle power demand prediction models. It is expected that it will be used in terms of operation and management from planning to install charging stations for electric vehicles using this model in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.697-700
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• 2005

최근들어 전력의 안정적인 공급과 계통의 안정한 운용 등을 위해서 신뢰성 높은 전력수요예측의 필요성이 점차 증가하고 있다. 본 논문에서는 기존에 제시된 예측시스템보다 정확도가 높은 전력수요예측을 위해 카오스 이론과 퍼지 보산 알고리즘을 이용하여 전력수요량 예측시스템을 제안한다. 최대수요 전력 시계열 데이터를 수집하여 카오스 성질을 분석하여 이를 바탕으로 퍼지 알고리즘을 적용한 전력수요량 예측 시스템을 구성하고, 이 시스템을 통하여 얻어진 결과와 실제 데이터를 비교함으로서 시스템의 성능을 평가한다.

• Korean Journal of Construction Engineering and Management
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• v.20 no.3
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• pp.105-112
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• 2019

Due to abnormal high temperature, electric power demand has exceeded the backup power reserved for emergency case, hence, resulting in a major power outage. In today's overcrowded cities, the unexpected disruption in energy supply and demand is a major threat to the enormous economic damage and urban malfunctions. Existing methods for estimating the demand of the emergency power source do not lend themselves to predict the actual demand in the spatial dimension of the city. In addition, the reserve power is arbitrarily distributed in the case of emergency. This paper presents a method that predicts the emergency power demand using the spatial distribution of emergency power demand by applying the daily energy consumption intensity and emergency power demand according to urban spatial information and building use.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.395-406
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• 2017

The purpose of this study is to identify the pattern of daily electricity demand through clustering and classification. The hourly data was collected by KPS (Korea Power Exchange) between 2008 and 2012. The time trend was eliminated for conducting the pattern of daily electricity demand because electricity demand data is times series data. We have considered k-means clustering, Gaussian mixture model clustering, and functional clustering in order to find the optimal clustering method. The classification analysis was conducted to understand the relationship between external factors, day of the week, holiday, and weather. Data was divided into training data and test data. Training data consisted of external factors and clustered number between 2008 and 2011. Test data was daily data of external factors in 2012. Decision tree, random forest, Support vector machine, and Naive Bayes were used. As a result, Gaussian model based clustering and random forest showed the best prediction performance when the number of cluster was 8.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.275-284
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• 2016

Accurate electricity demand forecasts is essential in reducing energy spend and preventing imbalance of the power supply. In forcasting electricity demand, we considered double seasonal Holt-Winters model and TBATS model with sliding window. We selected a specific time zone as the reference line of daily electric demand because it is least likely to be influenced by external factors. The forecasting performance have been evaluated in terms of RMSE and MAPE criteria. We used the observations ranging January 4, 2009 to December 31 for testing data. For validation data, the records has been used between January 1, 2012 and December 29, 2012.

• 전기의세계
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• v.66 no.12
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• pp.41-49
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• 2017

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1999.05a
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• pp.139-144
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• 1999

이땅에 근대화, 산업화가 시작되면서 전력수요량은 계속 증가 일로를 건고 있다. 최근에 IMF의 영향으로 전력수요의 증가세가 주춤한 것은 사실이나 점차 회복기에 들고 있는 우리 경제 사정을 감안할 때 그 수요가 계속 증가할 것임은 명약관화하다. 또한 전력 수요의 분포가 겨울보다는 여름에, 야간보다는 주간에 집중하는 전력수요의 편중현상도 뚜렷해지고 있다.(중략)

• Environmental and Resource Economics Review
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• v.25 no.2
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• pp.141-178
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• 2016

This paper, using AMR (Automatic Meter Reading) electricity data accurately measured in real time, analyses the characteristics and patterns of temperature effect on the industrial electricity usage. For this goal, the paper constructs and estimates a model which captures the properties of AMR time series including long-term trends, mid-term temperature effects, and short-term special day effects. Based on the estimated temperature response function and the temperature effect, we categorize the whole industry into two groups: one group with sharp temperature effect and the other with weak temperature effect. Furthermore, the industry group with sharp temperature effect is classified into a summer peak industry group and a winter peak industry group, based on the estimates of the temperature response function. These empirical results carry practical policy implications on the real time electricity demand management.

• Environmental and Resource Economics Review
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• v.31 no.1
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• pp.85-112
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• 2022

This study analyzed the influence of domestic and Chinese factors on fine dust(PM10 & PM2.5) in Korea by using the panel quantile regression. Daily analysis was conducted for 11 regions in Korea. For domestic factors, electricity demand and traffic volume, and for Chinese factors, interaction term of Chinese three cities' fine dust and the domestic west wind are used. As a result, the influence of domestic factors was different when the domestic fine dust concentration was high and low. When the fine dust concentration was low, electricity demand had a positive effect only on PM2.5, and didn't affect PM10 in the national analysis. In regional analysis, the amount of electricity demand had a significant effect on fine dust and ultrafine dust only in the capital area and Chungcheong. Electricity demand was found to significantly increase both PM2.5 and PM10 when it was high. On the other hand, it was confirmed that the Chinese factor always had a significant effect regardless of the concentration of PM10 and PM2.5. Therefore, in order to solve the problem of high concentration of fine dust, in addition to international cooperation, the reduction of PM2.5 generated by domestic thermal power generation should also be strengthened compared to the present.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1994.11a
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• pp.105-110
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• 1994

국내 총 71개 상용열병합발전업체를 대상으로 운전행태를 분석하고 이를 모형화하기 위한 설문조사 결과 이에 응답한 46개 업체의 운전행태를 분석해 보았으며 이들을 다시 10개 업종으로 구분하여 해당 업종의 전력수요량, 발전량, 수전량의 계절별, 시간대별 형태를 분석하였다. 그리고 분석기준치를 이용하여 향후 전력예측의 자료를 제공하였다. 업종별 발전행태에 의한 5개의 모형과 용량에 따른 5개의 모형을 도출하여 전원개발계획시 분산형전원을 고려할 수 있는 토태를 마련하였다.