Power system load data models and disaggregation based on sparse approximations

Rahul Sinha, S. Spoorthy, Prerna Khurana, M. Chandra
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引用次数: 3

Abstract

The deployment of smart meters by utilities holds the promise of improvements in operational efficiency, reliability and cost savings. With power measurements from smart meters, utilities can deploy innovative programs that allow end users to better control their energy usage while simultaneously reducing peak demand across the grid. In this paper, to develop data analysis tools for applications enabling monitoring and control of energy, a systems approach is taken, comprising of modeling, measurement, calibration and inference on the energy data collected from end users. A combination of analysis and synthesis for deriving data and measurement models calibrated to the aggregate power under measurement allows detection and estimation of features of individual appliances. Test results on disaggregation of power waveforms using the publicly available REDD data sets show promising results. The generic modeling and optimization framework can be used in the design and deployment of cyber physical energy systems for monitoring and control of energy resources.
基于稀疏逼近的电力系统负荷数据模型与分解
公用事业公司部署智能电表有望提高运营效率、可靠性和节约成本。通过智能电表的电力测量,公用事业公司可以部署创新的计划,使最终用户能够更好地控制他们的能源使用,同时减少整个电网的高峰需求。在本文中,为了开发用于监测和控制能源应用的数据分析工具,采用了一种系统方法,包括对从最终用户收集的能源数据进行建模、测量、校准和推断。将分析和综合相结合,用于导出数据和测量模型,以校准测量下的总功率,从而可以检测和估计单个设备的特征。使用公开可用的REDD数据集对功率波形进行分解的测试结果显示出有希望的结果。该通用建模和优化框架可用于网络物理能源系统的设计和部署,以实现能源资源的监测和控制。
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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