基于阶乘隐马尔可夫模型的非侵入式负荷监测方法

Gautam A. Raiker, B. S. Reddy, L. Umanand, Aman Yadav, Mujeefa M. Shaikh
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引用次数: 12

摘要

我们衡量的东西,我们可以改进。根据这种方法,班加罗尔印度科学研究所(IISc)通过安装智能电表在校园开发了一个微电网监测系统,覆盖了近250个节点,包括变电站、中心、部门、行政、宿舍和其他公用事业。这将在容量规划、变电站负荷、相位不平衡校正、过电压监测、计费等方面为研究所提供帮助。智能电表测量建筑物中单个点的电力消耗,从而显示整个建筑物的能源消耗情况。还需要了解安装智能电表的地方的组成负载情况,以便找到减少消耗的方法。个性化、简洁和可靠的反馈提供了设备层面的能源消耗分解,这是实施能源效率计划的关键。在此基础上,对非侵入式负荷监测(NILM)进行了研究。在NILM中,通过机器学习技术将汇总的智能电表数据分离为组成负载。NILM系统通过先前的数据集进行训练,然后算法将根据其经验将总功率分解为单个电器。采用一种称为阶乘隐马尔可夫模型的NILM基准算法来进行适当的负载分解。最后,我们尝试开发一款智能手机应用程序,将结果可视化,并将数据带给人们。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Approach to Non-Intrusive Load Monitoring using Factorial Hidden Markov Model
What we measure, we can improve. In accordance to this approach, Indian Institute of Science (IISc), Bangalore has developed a Micro-grid Monitoring System in the campus through the installation of Smart Meters, covering almost 250 nodes including substations, centers, departments, administration, hostels and other utilities. This will help the institute in various ways such as capacity planning, substation loading, phase imbalance correction, over-voltage monitoring, billing and so on. Smart Meters measure the power consumption at a single point in the building giving a picture of the energy consumption of the building as a whole. It is necessary to also understand the scenario of the constituent loads at the point where the smart meter is installed so that ways could be found to reduce consumption. Personalised, concise and reliable feedback providing appliance level breakdown of energy consumption in the premises is the key in implementing energy efficiency programs. Taking this into consideration the area of Non Intrusive Load Monitoring (NILM) was explored. In NILM the aggregate smart meter data is separated into constituent loads by machine learning techniques. The NILM system is trained through previous data sets and then the algorithm will disaggregate the total power into individual appliances based on its experience. A benchmark NILM algorithm called Factorial Hidden Markov Model was used for proper load disaggregation. Finally an attempt was made to develop a Smartphone app to visualize results and bring the data to the people.
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