• 전기의세계
• /
• 제31권5호
• /
• pp.368-374
• /
• 1982

This paper describes a combined algorithm for short-term-load forecating. One of the specific features of this algorithm is that the base, weather sensitive and residual components are predicted respectively. The base load is represented by the exponential smoothing approach and residual load is represented by the Box-Jenkins methodology. The weather sensitive load models are developed according to the information of temperature and discomfort index. This method was applied to Korea Electric Company and results for test periods up to three years are given.

• 전기학회논문지
• /
• 제58권1호
• /
• pp.1-6
• /
• 2009

The traditional demand prediction was based on the technique wherein electric power corporations made monthly or seasonal estimation of electric power consumption for each area and subscription type for the next one or two years to consider both seasonally generated and local consumed amounts. Note, however, that techniques such as pricing, power generation plan, or sales strategy establishment were used by corporations without considering the production, comparison, and analysis techniques of the predicted consumption to enable efficient power consumption on the actual demand side. In this paper, to calculate the predicted value of electric power consumption on a short-term basis (15 minutes) according to the amount of electric power actually consumed for 15 minutes on the demand side, we performed comparison and analysis by applying a 15-minute interval prediction technique to the average and that to the time series analysis to show how they were made and what we obtained from the simulations.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2009년도 추계학술대회
• /
• pp.720-722
• /
• 2009

경제 성장에 따른 국내 산업분야의 발달 및 국민 생활수준의 향상으로 전력 소비가 지속적으로 증가하고 있다. 전력을 안정적으로 공급하기 위해서는 전력 수요에 대한 중 단기 예측이 중요하며, 정확한 예측에 따라 안정적인 수급 계획을 확립할 수 있다. 본 논문에서는 부산시에서 공급되는 부산지역의 전력 데이터와 기후 관련 자료를 1995년 1월부터 2007년 12월까지의 측정치를 가지고 시계열 데이터를 수집하여 분석하고 신경회로망의 구조를 설계하여 실험을 통하여 실제 데이터와 예측 데이터를 비교 분석하고 평가한다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제16권3호
• /
• pp.163-172
• /
• 2016

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

• 대한산업공학회지
• /
• 제28권1호
• /
• pp.63-75
• /
• 2002

To improve the efficiency of the electric power generation, monthly maximum electric power consumptions for a next one year should be forecasted in advance and used as the fundamental input to the yearly electric power-generating master plan, which has a greatly influence upon relevant sub-plans successively. In this paper, we analyze the past 22-year hourly maximum electric load data available from KEPCO(Korea Electric Power Corporation) and select necessary data from the raw data for our model in order to reflect more recent trends and seasonal components, which hopefully result in a better forecasting model in terms of forecasted errors. After analyzing the selected data, we recommend to KEPCO the Winters' multiplicative model with decomposition and exponential smoothing technique among many candidate forecasting models and provide forecasts for the electric power consumptions and their 95% confidence intervals up to December of 1999. It turns out that the relative errors of our forecasts over the twelve actual load data are ranged between 0.1% and 6.6% and that the average relative error is only 3.3%. These results indicate that our model, which was accepted as the first statistical forecasting model for monthly maximum power consumption, is very suitable to KEPCO.

• 인터넷정보학회논문지
• /
• 제21권5호
• /
• pp.119-127
• /
• 2020

국내 전기차 (EV: Electric Vehicle) 시장이 성장함에 따라, 빠르게 증가하는 EV 충전 수요에 대응하기 위한 충전설비의 확충이 요구되고 있다. 이와 관련하여, 종합적인 설비 계획을 수립하기 위해서는 미래 시점의 충전 수요량을 예측하고 이를 바탕으로 전력설비 부하에 미치는 영향을 체계적으로 분석하는 것이 필요하다. 본 논문에서는 한국전력공사의 EV 충전 데이터를 이용하여 충전소 단위의 일별최대부하를 예측하는 LSTM(Long Short-Term Memory) 신경망 모델을 설계 및 개발한다. 이를 위해, 먼저 데이터 전처리 및 이상치 제거를 통해 정제된 데이터를 얻는다. 다음으로, 충전소 단위의 일별 특징들을 추출하여 훈련 데이터 집합을 구성하여 일별 최대 전력부하 예측 모델을 학습시킨다. 마지막으로 충전소 유형 별 테스트 집합을 이용한 성능 분석을 통해 예측 모델을 검증하고 이의 한계점을 논의한다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제13권1호
• /
• pp.39-49
• /
• 2013

This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of the current prediction and the change in the errors between the current and the previous prediction. The optimal weights (called "gains" in the error correction) are identified by differential evolution. The peak load forecasted by the RBF network models is also corrected by combining the load curve outputs of the RBF models by linear addition with 24 coefficients. The optimal coefficients for reducing both the forecasting mean absolute percent error (MAPE) and the sum of errors are also identified using differential evolution. The proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange. Simulation results reveal satisfactory forecasts: 1.230% MAPE for daily peak load and 1.128% MAPE for daily load curve.

• 대한전기학회논문지
• /
• 제32권6호
• /
• pp.191-198
• /
• 1983

This paper describes the aplication of different model which can be used for short-term load prediction. The model is based on Bohlin's approach to first develop a load profile model representing the nominal load component and the Box-Jenkins approach is used to predict residuals. An on-line algorithm using Kalman Filter and Time Series is implemented for and hour-ahead prediction. In the Kalman Filter system equation and measurement equation were fixed and parameters of Time Series were varied week after week. A set of data for Korea Electric Power Corporation from April to June 1981 was used for the evaluation of the model. As the result of this simulation 1.2% rms error was acquired.

• 한국융합학회논문지
• /
• 제9권5호
• /
• pp.1-6
• /
• 2018

스마트그리드에서 정확한 단기 부하 예측을 통한 자원의 이용 계획은 에너지 시스템 운영의 불확실성을 줄이고 운영 효율을 높이는데 있어서 매우 중요하다. 단기 부하 예측에 얕은 신경회로망을 포함한 다수의 머신 러닝 기법이 적용되어왔지만 예측 정확도의 개선이 요구되고 있다. 최근에는 컴퓨터 비전이나 음성인식 분야에서 심층 신경회로망의 뛰어난 연구 결과로 인해 심층 신경회로망을 단기 전력수요 예측에 적용해 예측 정확도를 개선하려는 시도가 주목 받고 있다. 본 논문에서는 일별 전력 부하 첨두치를 예측하기 위한 다층신경회로망 구조의 심층 신경회로망 모델을 제안한다. 제안된 심층 신경회로망은 층별 학습이 선행된 후 전체 모델의 학습이 이루어진다. 한국전력거래소에서 얻은 4년 동안의 일별 전력 수요 데이터를 사용, 하루 및 이틀 앞선 전력수요 첨두치를 예측하는 심층 신경회로망 모델을 구축하고 예측 정확도를 비교, 평가한다.

• 에너지공학
• /
• 제6권1호
• /
• pp.68-76
• /
• 1997

본 논문에서는 단기 부하예측을 위하여 인공신경망 모형을 제안하였다. 본 논문에서 제안된 인공신경망의 학습알고리즘은 기존의 역전파 알고리즘 보다 효과적으로 학습수렴이 빠르며 모수결정과 초기가중치 값들에 대한 의존도가 낮은 동적 적응 학습알고리즘을 개발하여 단기 부하예측에 그 적용 가능성을 시험하였다.