• 경영과학
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• 제26권1호
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• pp.77-91
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• 2009

It is the most important sucess factor for the electricity generation industry to minimize operations cost of surplus electricity generation through accurate demand forecasts. Temperature forecast is a significant input variable, because power demand is mainly linked to the air temperature. This study estimates the information value of the temperature forecast by analyzing the relationship between electricity load and daily air temperature in Korea. Firstly, several characteristics was analyzed by using a population-weighted temperature index, which was transformed from the daily data of the maximum, minimum and mean temperature for the year of 2005 to 2007. A neural network-based load forecaster was derived on the basis of the temperature index. The neural network then was used to evaluate the performance of load forecasts for various types of temperature forecasts (i.e., persistence forecast and perfect forecast) as well as the actual forecast provided by KMA(Korea Meteorological Administration). Finally, the result of the sensitivity analysis indicates that a $0.1^{\circ}C$ improvement in forecast accuracy is worth about $11 million per year.

• 전기학회논문지
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• 제63권11호
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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.

• International journal of advanced smart convergence
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• 제10권4호
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• pp.59-65
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• 2021

Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.

• 산업경영시스템학회지
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• 제43권2호
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• pp.1-13
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• 2020

There are several methods of peak-shaving, which reduces grid power demand, electricity bought from electricity utility, through lowering "demand spike" during On-Peak period. An optimization method using linear programming is proposed, which can be used to perform peak-shaving of grid power demand for grid-connected PV+ system. Proposed peak shaving method is based on the forecast data for electricity load and photovoltaic power generation. Results from proposed method are compared with those from On-Off and Real Time methods which do not need forecast data. The results also compared to those from ideal case, an optimization method which use measured data for forecast data, that is, error-free forecast data. To see the effects of forecast error 36 error scenarios are developed, which consider error types of forecast, nMAE (normalizes Mean Absolute Error) for photovoltaic power forecast and MAPE (Mean Absolute Percentage Error) for load demand forecast. And the effects of forecast error are investigated including critical error scenarios which provide worse results compared to those of other scenarios. It is shown that proposed peak shaving method are much better than On-Off and Real Time methods under almost all the scenario of forecast error. And it is also shown that the results from our method are not so bad compared to the ideal case using error-free forecast.

• Communications for Statistical Applications and Methods
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• 제19권6호
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• pp.869-876
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• 2012

In this paper, we applied seasonal time series models such as ARIMA, FARIMA, AR-GARCH and Holt-Winters in consideration of seasonality to forecast short-term electricity demand data. The results for performance evaluation on the time series models show that seasonal FARIMA and seasonal Holt-Winters models perform adequately under the criterion of Mean Absolute Percentage Error(MAPE).

• 제어로봇시스템학회논문지
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• 제8권11호
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• pp.948-953
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• 2002

The electric power load during the peak time in summer is strongly affected by cooling load, which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice-storage system and heat pump system are possible solutions to settle this problem. In this study. the method of estimating temperature and humidity to forecast the cooling load of ice-storage system is suggested, then the method of forecasting the cooling load using fuzzy logic is suggested by simulating that the cooling load is calculated using actual temperature and humidity. The forecast of the temperature, humidity and cooling load are simulated, and it is shown that the forecasted data approach to the actual data. Operating the ice-storage system by the forecast of cooling load with night electric power will improve the ice-storage system efficiency and reduce the peak electric power load during the summer season as a result.

• 설비공학논문집
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• 제14권8호
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• pp.626-633
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• 2002

The electric power load during the peak time in summer is strongly affected by cooling load, which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice-storage system and heat pump system etc. are used to settle this problem. In this study, the method of estimating temperature and humidity to forecast the cooling load of ice storage system is suggested. And also the method of forecasting the cooling load using neural network is suggested. For the simulation, the cooling load is calculated using actual temperature and humidity, The forecast of the temperature, humidity and cooling load are simulated. As a result of the simulation, the forecasted data is approached to the actual data.

• 전기학회논문지
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• 제67권8호
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• pp.1019-1023
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• 2018

In this paper sensitivity analysis of temperature on special day electricity demand of land and Jeju Island is performed. The basic electricity demand per 3 hours is defined as electricity demand that reflects the GDP effect without the temperature influence. The temperature sensitivity per 3 hours is calculated through the relationship between special day electricity demand normalized to basic electricity demand and temperature. In the future, forecast error will be improved if the temperature sensitivity per 3 hours is applied to the special day load forecasting.

• 에너지공학
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• 제18권2호
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• pp.114-124
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• 2009

지식경제부(MKE)는 매2년마다 전력수급기본계획을 수립한다. 본 논문에서는 전력수급기본계획 수립시 전력수요를 과대 또는 과소로 예측한 것이 차기 전력수급기본계획 수립시 전원혼합(Energy Mix)에 미치는 영향을 정량적으로 평가하였다. 전력수요 자료는 2005년도에 예측한 제3차 전력수급기본계획의 전망치를 이용하였고 전원혼합을 도출하기 위하여 전력거래소(KPX)에서 활용하고 있는 WASP 전산모형을 단순화한 시뮬레이션 모형을 구축하였다. 2005년도 전력수요를 적정, 5% 과대 그리고 5% 과소 예측한 경우에 대하여 각각 단순화한 시뮬레이션 모형을 이용하여 2005년도 전력수급기본계획의 전원혼합을 도출하였다. 이 3가지 전원혼합을 초기조건으로 하여 2005년도의 적정 전력수요가 2007년 이후에 적용된다고 보고 2007년도에 차기 전력수급기본계획의 전원혼합을 도출하였다. 전력수요가 적정일 경우, 2005년도와 2007년도 전력수급 기본계획의 전력수요는 동일하므로 전원혼합에 변화가 없다. 전력수요를 5% 과대 또는 5% 과소 예측한 경우, 계획된 발전소 건설을 차기 전력수급기본계획 수립시 줄이거나 늘려야 하는데 건설기간이 짧은 LNG 발전소가 그 영향을 받는 것으로 나타났다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
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• pp.843-845
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• 2005

The uncertainty of long term electricity plan consists of the uncertainty of demand forecast and additional generating capacity. Demand forecast is clearly improved the accuracy than the past through improving forecasting methods. However, the uncertainty of additional generating capacity is increased due to the change of market environment. In an operation by a sole utility, additional generating capacity would be possible by the regulation of government. Currently the generation companies have spined off from KEPCO and some IPPs participate the electricity market. It increases the uncertainty due to weakened regulation. Also the environment movement by NGOs and occurrence of civil affairs cause the increase of uncertainty. This research would analyze the current situation on the uncertainty of additional generating capacity and construction delays. Furthermore this research would present the plan to reflecting it in long term electricity plan.