Title :
Application of Combination Forecasting Based on Rough Sets Theory on Electric Power System
Author :
Bao, Yidan ; Huang, Min ; Zhu, Zheyan ; He, Yong ; Li, Xiaoli
Author_Institution :
Coll. of Biosystems Eng. & Food Sci., Zhejiang Univ.
Abstract :
The problem of determination to weighting coefficient is a key and difficulty for combination forecast. The result of the forecast will be straightly influenced if the selection of the weighting coefficient is illogicality. A new method of combination forecasting applied in electric power system is showed in this paper. It is based on the rough sets theory, and the weighting coefficient of all the forecast methods is distributed, so that the calculation of the weighting coefficient will be more impersonal and simple, and the result of the forecast will be more exactly. In this paper, two samples were used to check the accuracy of this method, the Percent of errors were 1.76% and 2.3197%. Compared with another method for combination forecasting- artificial neutral network, the Percent of errors were 16.213% and 35.7084%, respectively
Keywords :
load forecasting; power systems; rough set theory; combination forecasting; electric power system; rough sets theory; weighting coefficient; Costs; Economic forecasting; Electricity supply industry; Helium; Load forecasting; Neural networks; Predictive models; Rough sets; Set theory; Uncertainty; Combination forecasting; Electric power system; Power load; Rough sets theory; Weighting coefficient;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1712652
