The maximum power demand forecasting with fuzzy theory

Author

Lee, Ming-Rong ; Wang, Shun-Jih ; Yi-Yu, Lu ; Tai, Liang-I ; Shi, Hao-Jun

Author_Institution

Dept. of Electr. Eng., Far East Univ., Tainan, Taiwan

Volume

2

fYear

2010

fDate

5-7 May 2010

Firstpage

419

Lastpage

422

Abstract

The study aims at seeking the interrelationship and origin of the basic electricity, mobile electricity, power adjustment charges, additional super-charges, and line subsidy payments of the high-pressure two-stage electricity users (Far East University), according to the average of monthly temperature and tariff structure calendar year. Using fuzzy theory to analyze and simulate the peak electricity quantity of kilowatt of entire year, then using genetic algorithm to this system for making the best learning. Assist users to find the optimal contracted capacity with this way to achieve the goal of saving electricity cost.

Keywords

fuzzy set theory; genetic algorithms; load forecasting; fuzzy theory; genetic algorithm; maximum power demand forecasting; mobile electricity; peak electricity quantity; power adjustment charge; Automation; Contracts; Cost function; Demand forecasting; Educational institutions; Energy consumption; Genetic algorithms; Load forecasting; Power demand; Reactive power; fuzzy theory; genetic algorithm (GA); optimal contracted capacity;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Communication Control and Automation (3CA), 2010 International Symposium on

Conference_Location

Tainan

Print_ISBN

978-1-4244-5565-2

Type

conf

DOI

10.1109/3CA.2010.5533319

Filename

5533319