AREA 활용 전력수요 단기 예측

• 영문명: Short-term Forecasting of Power Demand based on AREA
• 발행기관: 한국산업경영시스템학회
• 저자명: 권세혁(S. H. Kwon) 오현승(H. S. Oh)
• 간행물 정보: 『산업경영시스템학회지』제39권 제1호, 25~30쪽, 전체 6쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2016.03.30

영문 초록

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.

목차

Abstract

1. 서론

2. 연구방법

3. 실증분석

4. 결론

Acknowledgement

키워드

SARMA Intervention Model Forecasting MAPE Power Demand Seasonality