基于模糊遗传算法的年电力需求预测方法

Wen-Bin Lin, Chia-Ching Lin, H. Chiang, Chien-An Chen, Liang-I Tai
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引用次数: 0

摘要

本文以两类工业级高压用户为研究对象,根据以往的月度电费计算结构,研究了需求费、能源费、功率因数费和罚没费等各类电费,以及它们之间的相互关系。采用模拟模糊理论分析和遗传算法的最优学习方法,以年峰值负荷为关键参数,推导出最优合约容量。工业级消费者可以预测工厂运行耗电量,以实现节能目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Annual Power Demand Prediction Approach by Fuzzy-Genetic Algorithm
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.
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