• 대한전기학회논문지:전력기술부문A
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• 제49권8호
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• pp.380-389
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• 2000

This paper presents a method for the regional long-term load forecasting in metropolitan area considering econimic indicator with the assumption that energy demands propoprtionally increases under the economic indicators. For the accurate load forecasting, it is very important to scrutinize the correlation among the regional electric power demands, economic indicator and other characteristics because load forecasting results may vary depending on many different factors such as electric power demands, gross products, social trend and so on. Three steps for the regional long-term load forecasting are microscopically and macroscopically used for the regional long -term load forecasting in order to increase the accuracy and practicality of the results.

• 전기학회논문지
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• 제64권9호
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• pp.1276-1280
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• 2015

Among the various statistical factors for South Korea, the population has been steadily decreased by lower birthrate. Nevertheless, the number of household is constantly increasing amid population aging and single life style. In general, residential electricity use is more the result of the number of household than the population. Therefore, residential electricity consumption is expected to be far higher for decades to come. The existing long-term load forecasting, however, do not necessarily reflect the growth of single and two-member households. In this respect, this paper proposes the long-term load forecasting for residential users considering the effect of changes of the housing type, and in the case study the changes of the residential load pattern is analyzed for accurate long-term load forecasting.

• 한국산학기술학회논문지
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• 제16권12호
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• pp.8576-8584
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• 2015

전력수요의 예측은 안정적인 전력공급을 위한 수급계획수립을 위해서 그리고 전력계통의 최적운영계획수립을 위해서도 필요하다. 특히 안정적인 전력수급확보를 위해서는 중장기 전력수요예측이 중요하고 공급안정성 강화를 위해서는 지역별 전력수요예측이 중요하다. 지역별 전력수요예측은 지역에 소요되는 부하를 충족시킬 수 있도록 송전선로 및 변전소 등의 계통망의 최적상태 구성 및 유지를 위한 필수적인 과정으로 알려져 있다. 따라서 본 논문은 12개월(중장기)동안 대한민국 시도별 16개 지역의 전력사용량을 SARIMA 모형을 이용하여 예측하는 방법을 제안한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 C
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• pp.1163-1165
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• 1998

This paper presents a method of the regional long-term load forecasting considering economic indicator with the assuption that energy demands proportionally increases with the economic indicators. For the accurate load forecasting, it is very important to scrutinize the correlation among the regional electric power demands, economic indicator and other characteristics because load forecasting results may vary depending on many different factors such as electric power demands, gross products, social trend and so on. Three steps are microscopically and macroscopically used for the regional long-term load forecasting in order to increase the accuracy and practically of the results.

• 대한전기학회논문지:전력기술부문A
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• 제53권11호
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• pp.604-609
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• 2004

This paper suggests a long-term distribution area load forecasting algorithm which offers basic data for distribution planning of power system. To build forecasting model, 4-level hierarchical spatial structure is introduced: System, Region, Area, and Substation. And, each spatial load can be decided proportional to its portion in the higher level. This paper introduces the horizon year loads to improve the forecasting results. And, this paper also introduces an effective load transfer algorithm to improve forecasting stability in case of new or stopped substations. The proposed model is applied to the load forecasting of KEPCO system composed of 16 regions, 85 areas and 761 substations, and the results are compared with those of econometrics model to verify its validity.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.495-497
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• 2000

The KEPCO is developing the load forecasting sysetm using land use simulation method and distribution planning system. Distribution planning needs the data of presents loads, forecasted loads sub-statin, and distribution lines. Using the data, determine the sub-station and feeder lines according to the load forecasting data. This paper presents the method of formulation processfor the long term load forecasting and optimal distribution planning and optimal distribution planning. And describes the case study of long term distribution planning of Kwangju city accord to the newly applied method.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.77-87
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• 2021

In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.

• 대한전기학회논문지
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• 제34권10호
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• pp.389-398
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• 1985

Recently maximum power demand of our country has become to be under the great in fluence of electric cooling and air conditioning demand which are sensitive to weather conditions. This paper presents the technique and algorithm to forecast the long-term maximum power demand considering the characteristics of electric power and weather variable. By introducing a weather load model for forecasting long-term maximum power demand with the recent statistic data of power demand, annual maximum power demand is separated into two parts such as the base load component, affected little by weather, and the weather sensitive load component by means of multi-regression analysis method. And we derive the growth trend regression equations of above two components and their individual coefficients, the maximum power demand of each forecasting year can be forecasted with the sum of above two components. In this case we use the coincident dry bulb temperature as the weather variable at the occurence of one-day maximum power demand. As the growth trend regression equation we choose an exponential trend curve for the base load component, and real quadratic curve for the weather sensitive load component. The validity of the forecasting technique and algorithm proposed in this paper is proved by the case study for the present Korean power system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1447-1449
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• 1999

The KEPCO is developing the load forecasting system using land-use simulation method and distribution planning system. A distribution planning needs the data of present loads, forecasted loads and substations. distribution lines information. By the distribution planning system, the distribution line designer determines the substations and feeder lines plan. This paper presents the method of formulation process for the long term load forecasting and optimal distribution planning, and describes the case study of long term distribution planning of Suwon-city according to the newly applied method.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.1037-1040
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• 2000

We introduce the forecasting method for a next day electric peak load that uses the optimal combination of two types of neural networks. First network uses learning data that are past 10days of the target day. We name the neural network Short Term Neural Network (STNN). Second network uses those of last year. We name the neural network Long Term Neural Network (LTNN). Then we get the forecasting results that are the linear combination of the forecasting results by STNN and the forecasting results by LTNN. We name the method Combination Forecasting Method (CFM). Then we discuss the optimal combination of STNN and LTNN. Using CFM of the optimal combination of STNN and LTNN, we can reduce the forecasting error.