AREA 활용 전력수요 단기 예측 = Short-term Forecasting of Power Demand based on AREA

다국어 초록 (Multilingual Abstract)

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer’s perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. ARMA(2, 1, 2)(1, 1, 1)7 and ARMA(0, 1, 1)(1, 1, 0)12 are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

참고문헌 (Reference)

1 심성철, "제한적 시장을 가지는 천연자원의 가격예측 모형에 관한 연구" 한국산업경영시스템학회 37 (37): 82-89, 2014

2 정현우, "전력계통 유지보수 및 운영을 위한 향후 4주의 일 최대 전력수요예측" 대한전기학회 63 (63): 1497-1502, 2014

3 송경빈, "시간대별 기온을 이용한 전력수요예측 알고리즘 개발" 대한전기학회 63 (63): 451-454, 2014

4 권세혁, "보 지역 홍수 위험도 예측모형 연구" 한국산업경영시스템학회 37 (37): 91-95, 2014

5 최상열, "데이터마이닝을 이용한 단기부하예측" 한국조명.전기설비학회 21 (21): 126-133, 2007

6 박영진, "뉴로-퍼지 모델 기반 전력 수요 예측 시스템: 시간,일간,주간 단위 예측" 한국지능시스템학회 14 (14): 533-538, 2004

7 Box, E. P., "Time Series Analysis : Forecasting and Control" Wiley 2015

8 Cheol-Hong Kim, "Short-term Electric Load Forecasting Using Data Mining Technique" 대한전기학회 7 (7): 807-813, 2012

9 Ministry of Governmen Legislation, "Procedure of forecasting power demand" National Law 2011

10 Kim, C. H., "Forecasting of Domestic Power Demand using Multiple Seasonal Exponential Smoothing Techniques, The annual report of Korea Enright Economics Institute" 2013

11 Lee, H.R., "Electricity Demand Forecasting based on Machine Learning Algorithms" Korea Academic Association of Business Administration

12 Oh, H. S., "A comparison of technological growth models" 22 (22): 51-68, 1994

13 Korea Power Exchange, "A Study on the criteria of the electricity demand forecast evaluation and the confidence interval, annual report of 2011"