Title :
The Annual Power Demand Prediction Approach by Fuzzy-Genetic Algorithm
Author :
Lin, Wen-Bin ; Lin, Chia-Ching ; Chiang, Huann-Keng ; Chen, Chien-An ; Tai, Liang-I
Author_Institution :
Grad. Sch. of Eng. Sci. & Tech., Nat. Yunlin Univ. of Sci. & Technol., Douliou, Taiwan
Abstract :
This paper focus on two types of the industry class high-voltage consumer, to investigate various kind of electricity fees which includes demand charge, energy charge, power factor charge and penalty charge, and correlation among them according to the monthly electricity fee calculation structure in the past. Using the simulation of Fuzzy theory analysis and the Optimal Learning of Genetic Algorithm method, the optimal contract capacity can be derived by selecting annual peak load as a key parameter. The Industrial Class Consumer can predict the plant operation power consumption to fulfill energy conservation goal.
Keywords :
electricity supply industry; genetic algorithms; power system economics; Fuzzy theory analysis; annual peak load; annual power demand prediction approach; demand charge; electricity fee calculation structure; energy charge; energy conservation; fuzzy-genetic algorithm; industry class high-voltage consumer; optimal learning; penalty charge; power consumption; power factor charge; Books; Contracts; Electricity; Equations; Load modeling; Mathematical model; Power demand; Fuzzy theory; Genetic Algorithm; annual power demand; the optimum contract capacity;
Conference_Titel :
Genetic and Evolutionary Computing (ICGEC), 2010 Fourth International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4244-8891-9
Electronic_ISBN :
978-0-7695-4281-2
DOI :
10.1109/ICGEC.2010.62