• Environmental and Resource Economics Review
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• v.9 no.2
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• pp.333-347
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• 2000

We employed various econometic methods to estimate the production index elasticity and the price elasticity of elecricity demand in Korea and compared the forecasting power of those methods. Cointegration models (ADL model, Engle-Granger model, Full Informtion Maximum Likelihood method by Johansen and Juselius) and Dynamic OLS by Stock and Watson were considered. The forecasting power test shows that Dynamic OLS has the best forecasting power. According to Dynamic OLS, the production index elasticity and the price elasticity of electricity demand in Korea are 0.13 and -0.40, respectively.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.11
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• pp.1503-1510
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• 2014

This study organizes scenarios on the power supply plans and electricity load forecasts considering their uncertainties and estimates natural gas quantity for electricity generation, total electricity supply cost and air pollutant emission of each scenario. Also the analysis is performed to check the properness of government's natural gas demand forecast and the possibility of achieving the government's CO2 emission target with the current plan and other scenarios. In result, no scenario satisfies the government's CO2 emission target and the natural gas demand could be doubled to the government's forecast. As under-forecast of natural gas demand has caused the increased natural gas procurement cost, it is required to consider uncertainties of power plant construction plan and electricity demand forecast in forecasting the natural gas demand. In addition, it is found that CO2 emission target could be achieved by enlarging natural gas use and demand-side management without big increase of total costs.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.87-91
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• 1990

Regional electricity demand forecasting is among the most important step for lone-term investment and power supply planning. This study presents a regional electricity forecasting model for Korean power system. The model consists of three submodels, regional economy, regional electricity energy demand, and regional peak load submodels. A case study is presented.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.349-360
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• 2013

Forecasting the daily peak load for electricity demand is an important issue for future power plants and power management. We first introduce several time series models to predict the peak load for electricity demand and then compare the performance of models under the RMSE(root mean squared error) and MAPE(mean absolute percentage error) criteria.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.3
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• pp.35-46
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• 2013

In order to develop an accurate analytical model for domestic electricity demand forecasting, we propose a prediction method of the electric power demand pattern by combining SMO classification techniques and a dimension reduction conceptualized subspace clustering techniques suitable for high-dimensional data cluster analysis. In terms of electricity demand pattern prediction, hourly electricity load patterns and the demographic and geographic characteristics can be analyzed by integrating the wireless load monitoring data as well as sub-regional unit of census information. There are composed of a total of 18 characteristics clusters in the prediction result for the sub-regional demand pattern by using census information and power load of Seoul metropolitan area. The power demand pattern prediction accuracy was approximately 85%.

• 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.

• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.363-370
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• 2010

Load forecasting has always been essential to the operation and planning of power systems in deregulated electricity markets. Various methods have been proposed for load forecasting, and the neural network is one of the most widely accepted and used techniques. However, to obtain more accurate results, more information is needed as input variables, resulting in huge computational costs in the learning process. In this paper, to reduce training time in multi-layer perceptron-based short-term load forecasting, a graphics processing unit (GPU)-based computing method is introduced. The proposed approach is tested using the Korea electricity market historical demand data set. Results show that GPU-based computing greatly reduces computational costs.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.675-683
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• 2015

In the 21st century, we now face the serious problems of the enormous consumption of the energy resources. Depending on the power consumption increases, both China and South Korea face a reduction in available resources. This paper considers the regression models and time-series models to compare the performance of the forecasting accuracy based on Mean Absolute Percentage Error (MAPE) in order to forecast the electricity demand accurately on the short-term period (68 months) data in Northeast China and find the relationship with Korea. Among the models the support vector regression (SVR) model shows superior performance than time-series models for the short-term period data and the time-series models show similar results with the SVR model when we use long-term period data.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.289-291
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• 2000

In a competitive electricity power market, the price of electricity changes instantly, that of conventional market is predetermined and hardly changes. In such a new environment, customers' behaviors change instantly according to the changing electricity prices. If we develop a electricity load model that well describes the behavior of electricity consumers, we can utilize that model in forecasting the amount of future load, solving the load flow problem and finding the weak point of the system. In this paper new electricity model that considers the price of electricity and power factor of the load is presented. While conventional load model, which is demand function of electricity, uses the price of real and reactive power as the independent variable of the demand function. this new load model uses price of real power and penalty factor according to the power factor for the calculation of amount of electricity demand.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.432-434
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• 2002

Recently, electricity demand of remote islands in Korea has been rapidly increased. It's mainly due to increase of income level resulted from economic development. Electricity demand patterns and characteristics in remote islands are different from those of mainland in point of time of peak load, demographic and industrial characteristics of islands, and so on. The optimal power supply in remote islands has a important relationship with accurate analysis of island's load characteristics, the adoption of relevant load forecasting technique, and optimal power facilities reflecting local's electricity demand characteristics. This paper shows the recent load pattern and characteristics, load forecasting using probability distribution, and the perpetration of relevant power facilities in remote islands.