[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5207/JIEIE.2008.22.2.063

The Spatial Electric Load Forecasting Algorithm using the Multiple Regression Analysis Method

Nam, Bong-Woo (숭실대학교 전기공학부)
Song, Kyung-Bin (숭실대학교 전기공학부)
Kim, Kyu-Ho (안산공과대학 전기과)
Cha, Jun-Min (대진대학교 전기공학과)

Publication Information

Journal of the Korean Institute of Illuminating and Electrical Installation Engineers / v.22, no.2, 2008 , pp. 63-70 More about this Journal

Abstract

This paper resents the spatial electric load forecasting algorithm using the multiple regression analysis method which is enhanced from the algorithm of the DISPLAN(Distribution Information System PLAN). In order to improve the accuracy of the spatial electrical load forecasting, input variables are selected for GRDP(Gross Regional Domestic Product), the local population and the electrical load sales of the past year. Tests are performed to analyze the accuracy of the proposed method for Gyeong-San City, Gu-Mi City, Gim-Cheon City and Yeong-Ju City of North Gyeongsang Province. Test results show that the overall accuracy of the proposed method is improved the percentage error 11.2[%] over 12[%] of the DISPLAN. Specially, the accuracy is enhanced a lot in the case of high variability of input variables. The proposed method will be used to forecast local electric loads for the optimal investment of distribution systems.

Keywords

Spatial electric load forecasting; Local economy; Optimal investment; Distribution system;

Citations & Related Records

Reference

1	한국전력공사 전력연구원, '신경회로망을 이용한 배전부하 수요예측 연구', 2000 최종보고서
2	한국전력공사 전기연구원, 'A Study on the Improvement of Forecasting Method for Regional Demand and Load Characteristics', 1998
3	H. Lee Willis, ' Spatial Electric Load Forecasting', Marcel Dekker, New York, 2002
4	하성관, '신경회로망을 이용한 변압기의 단기전력수요예측', 숭실대학교 대학원 석사학위 논문, 2005.2
5	한국전력공사 전력연구원, '신뢰성 및 경제성을 고려한 배전계통 최적투자계획 수립 모델 및 시스템의 개발', 2006 1차년도 진도보고서
6	강근석, 김충락, '회귀분석', 교우사, 2001
7	DISPLAN
8	경상북도 통계DB, 'http://stat.gb.go.kr'
9	통계정보시스템, 'http://kosis.nso.go.kr'
10	경산시 통계연보, 'http://www.gyeongsan.go.kr'
11	구미시 통계연보, 'http://www.gumi.go.kr'
12	김천시 통계연보, 'http://www.gimcheon.go.kr'
13	영주시 통계연보, 'http://www.yeongju.go.kr'

Reference
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Reference

4	Hyoung-Ro Lee. (2011) IE interfaces Electricity Demand Forecasting based on Support Vector Regression / 24 (4) , 351
4	Pyeong-Shik Ji. (2013) The Transactions of the Korean Institute of Electrical Engineers P Development of Daily Peak Power Demand Forecasting Algorithm using ELM / 62 (4) , 169
3	Yong-San Park. (2014) The Transactions of the Korean Institute of Electrical Engineers P Development of Daily Peak Power Demand Forecasting Algorithm with Hybrid Type composed of AR and Neuro-Fuzzy Model / 63 (3) , 189
4	Pyeong-Shik Ji. (2014) The Transactions of the Korean Institute of Electrical Engineers P Development of Daily Peak Power Demand Forecasting Algorithm Considering of Characteristics of Day of Week / 63 (4) , 307
6	Sung-Yul Kim. (2014) The Transactions of The Korean Institute of Electrical Engineers Transmission Network Expansion Planning Using Risk Level Improvement Index / 63 (6) , 752
4	Dongwon Lee. (2015) Journal of Intelligence and Information Systems An Expert System for the Estimation of the Growth Curve Parameters of New Markets / 21 (4) , 17
2	Young-Eun Kim. (2017) New & Renewable Energy A Study on the Suitability of Load Demand Forecasting Models for Island Area Using Weather Variables / 13 (2) , 84
1	(2018) Journal of Society of Korea Industrial and Systems Engineering The Prediction and Analysis of the Power Energy Time Series by Using the Elman Recurrent Neural Network / 41 (1) , 84

KSCI

The Spatial Electric Load Forecasting Algorithm using the Multiple Regression Analysis Method 다중회귀분석법을 이용한 지역전력수요예측 알고리즘

The Spatial Electric Load Forecasting Algorithm using the Multiple Regression Analysis Method