• Environmental and Resource Economics Review
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• v.27 no.2
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• pp.287-313
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• 2018

This paper measures and analyzes cooling and heating demand in Korean electricity demand using time-varying temperature response functions and cooling and heating temperature effects. We fit the model to Korean data for residential and commercial sector over 1999:01~2016:12 and the estimation results show that the growth rate of heating demand is much higher than that of base and cooling demand, and especially the growth rate of heating demand in commercial sector is much higher. And we define the temperature-normalized demand conditioning that monthly temperatures are assumed as average monthly temperatures. The growth rate of heating demand in the estimated temperature-normalized demand is higher than that in the real demand. Our results are expected to be a base data for Winter Demand Management and short-term electricity demand forecasting.

• Journal of Energy Engineering
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• v.22 no.1
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• pp.1-7
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• 2013

This paper attempts to estimate the residential electricity demand function, using survey data of 521 households in Korea. As the residential electricity demand function provides us information on the pattern of consumer's electricity consumption, it can be usefully utilized in predicting the impact of policy variables such as electricity price and forecasting electricity demands. We apply least absolute deviation(LAD) estimation as a robust approach to estimating parameters. The results showed that price and income elasticities are -0.68 and 0.14 respectively, and statistically significant at the 10% levels. The price and income elasticities portray that residential electricity is price- and income-inelastic. This implies that the residential electricity is indispensable goods to human-being's life, thus the residential electricity demand would not be promptly adjusted to responding to price and/or income change.

• 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.19 no.3
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• pp.633-658
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• 2010

In this paper, we estimate the Translog cost function in Korean manufacturing, using capital (K), labor (L), material (M), electricity (E), fuel (F) data over the period from 1970 to 2005. Especially, this paper investigates the impact of imposing concavity in the estimation of a Translog cost function. Although the value of log-likelihood is somewhat reduced in a concavity imposed function rather than a function which is not, a concavity imposed function satisfies regularity conditions (monotonicity, positivity, concavity) at all data points. We also calculate price elasticities using a concavity imposed Translog cost function. Electricity complements capital so electricity demand increases as capital demand increases. Meanwhile, electricity substitutes labor, fuel, and material. These results show that Korean manufacturing experienced a structural change of increase in electricity demand.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.885-894
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• 2015

It is necessary to forecast the electricity demand for reliable and effective operation of the power system. In this study, we try to categorize a functional data, the mean curve in accordance with the time of daily power demand pattern. The data were collected between January 1, 2009 and December 31, 2011. And it were converted to time series data consisting of seasonal components and error component through log transformation and removing trend. Functional clustering by Ma et al. (2006) are applied and parameters are estimated using EM algorithm and generalized cross validation. The number of clusters is determined by classifying holidays or weekdays. Monday, weekday (Tuesday to Friday), Saturday, Sunday or holiday and season are described the mean curve of daily power demand pattern.

• Environmental and Resource Economics Review
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• v.24 no.2
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• pp.365-410
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• 2015

Block-rate structures are widely used in utility-pricing, including the Korean residential electricity sector. In the case of the current pricing structure, Korean citizens are highly concerned about incurring excessive electricity costs. For these reasons, there have been many discussions concerning mitigation of the strict pricing structure. Existing studies on the residential electricity demand function under block-rate structure have the following three issues - the consumer's budget constraint is non-linear, perceived price under block-rate structure is uncertain, block-rate structure has endogeneity in the price variable. In this context, this paper estimates the residential electricity demand function using micro-level household expenditure data and simulates the impact of alternative block-pricing schedules.

• Environmental and Resource Economics Review
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• v.23 no.1
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• pp.43-65
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• 2014

In this paper, we test for allocative efficiency of productive inputs including electricity and measure the divergence between the actual and optimal level of electricity for the chemical products, which is a relatively highly electricity-intensive sector in Korean manufacturing industries, by estimating a shadow cost function. Supposing cost minimization subject to market prices was achieved, we derive the price elasticities of demand for each input and simulate the impact of a 10% increase in power rate on its demand and supply price by estimating jointly a cost function with an inverse supply relation. The null hypothesis of allocative efficiency of inputs is rejected over the period 1982-2006. On average, electricity is used more than optimal level by 98% per year. The demand for electricity decreases by 11.4%, and supply price, on average, falls by 0.08%, other things being equal.

• Journal of Energy Engineering
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• v.25 no.2
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• pp.37-44
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• 2016

The demand for electricity has a considerable impact on various energy sectors since electricity is generated from various energy sources. This paper attempts to estimate the electricity demand function and obtain some quantitative information on price and income elasticities of the demand. To this end, we apply a lagged dependent variable model to derive long-run as well as short-run elasticities using the time-series data over the period 1991-2014. Our dependent variable is annual electricity demand. The independent variables include constant term, real price of electricity, and real gross domestic product. The results show that the short-run price and income elasticities of the electricity demand are estimated to be -0.142 and 0.866, respectively. They are statistically significant at the 5% level. That is, the electricity demand is in-elastic with respect to price and income changes in the short-run. The long-run price and income elasticities of the electricity demand are calculated to be -0.210 and 1.287, respectively, which are also statistically meaningful at the 5% level. The electricity demand is still in-elastic with regard to price change in the long-run. However, the electricity demand is elastic regarding income change in the long-run. Therefore, this indicates that the effect of demand-side management policy through price-control is restrictive in both the short- and long-run. The growth in electricity demand following income growth is expected to be more remarkable in the long-run than in the short-run.

• Environmental and Resource Economics Review
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• v.23 no.3
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• pp.409-434
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• 2014

This paper estimates household demand for electricity using a micro-level household expenditure data set. A two-stage estimation method where the endogenous block price estimates are obtained from a discrete block choice model is used. This method successfully identifies a downward sloping conditional demand function with the data, while both the usual two-stage method with instrumental variable estimation and the Hewitt-Hanemann discrete-continuous model fail to do that. The paper simulates the impacts of two hypothetical pricing reforms that reduce the number of blocks and make the price gap smaller. It is shown that the reform may increase the overall consumer benefit, but is regressive.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.157-158
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• 2019

발전소 관리의 단기 전력 수요에 대한 정확한 예측은 전력 시스템의 안전하고 효율적인 작동을 보장하는데 필수적이다. 따라서 본 연구는 가우스 커널 함수 네트워크 (GKFNs)의 심층 구조를 이용하여 일일 최대 전력 수요를 예측하는 새로운 방법을 제시한다. 제안 된 GKFN의 깊이 구조는 표준 GKFN에 비해 예측 정확도를 향상시킨다. 한국의 일일 최대 전력 수요를 예측하기위한 시뮬레이션은 제안 된 예측 모델이 GKFN 모델, k-NN 및 SVR과 같은 다른 예측 모델에 비해 예측 성능에 이점이 있음을 보여준다. GKFN의 제안된 심층 구조는 시계열 예측 및 회귀 문제의 다양한 문제에 적용될 수 있다.