需求侧管理负荷预测的人工神经网络方法

S. Vi, N.Ays warya
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引用次数: 3

摘要

为了满足快速增长的能源需求,需要采用符合环境和节能的智能技术。在本文中,一个自主的需求侧能源管理,鼓励用户在不影响服务质量和客户满意度的情况下自愿改变他们的电力消耗,使用负荷预测。预计的分布式需求侧能源管理(DSM)策略为每个消费者提供了一个选择,只需将其最佳响应策略应用于配电系统中当前的电力负荷和电价。利用神经网络和蚁群算法进行负荷预测,得出基于区域负荷的定价方法对电力公司和用户都有利。仿真结果表明,该方法可以最大限度地提高负荷系数,降低总能源成本和用户的日常电费。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Neural Network Approach for LoadForecasting in Demand Side Management
To meet the fast growing demand of energy, smart techniques need to be adopted that are in compliance with the environment and energy conservation. In this paper, an autonomous demand-side energy management to encourage users to willingly modify their electricity consumption without compromising with service quality and customer satisfaction using load forecasting. The projected distributed demand side energy management (DSM) strategy gives each consumer an option to simply apply its best response strategy to current electric Load and tariff in the power distribution system. Using NN and ACO technique on load prediction, it is obtained that an area-load based pricing method is beneficial for both electric utility and consumer. Simulation results shows that the proposed approach can maximize load factor and reduce total energy cost as well as user’s daily electricity charges.
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