Load signature study ¡V part II: Disaggregation framework, simulation and applications

Jian Liang, S. Ng, Gail Kendall, John W. M. Cheng
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引用次数: 13

Abstract

Load Signatures embedded in common electricity consumption patterns in fact could render much information pertaining to the nature of the appliances and their usage patterns. Based on the proposed disaggregation framework, we use three advanced disaggregation algorithms, called Committee Decision Mechanisms (CDMs), to perform load disaggregation at the metering level. Three random switching simulators are also developed to investigate the performance of different CDMs under a variety of scenarios. Through Monte Carlo simulations, we demonstrate that all CDMs outperform any single-feature, single-algorithm based disaggregation methods. With sensitivity analysis, we also show that the CDMs are less sensitive to any load dynamics and noise. We finally demonstrate some applications of this technology in terms of appliance usage tacking and estimated energy consumption of each appliance.
负载特征研究——第二部分:分解框架、仿真和应用
实际上,嵌入在普通电力消耗模式中的负载签名可以提供有关设备性质及其使用模式的许多信息。基于提出的解聚框架,我们使用了三种称为委员会决策机制(CDMs)的高级解聚算法来执行计量级的负载解聚。本文还开发了三个随机切换模拟器来研究不同cdm在各种场景下的性能。通过蒙特卡罗模拟,我们证明了所有cdm都优于任何基于单特征、单算法的解聚方法。通过灵敏度分析,我们还表明cdm对任何负载动态和噪声都不太敏感。最后,我们从设备使用跟踪和每台设备的估计能耗方面展示了该技术的一些应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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