• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE PES General Meeting, Conference Exposition >An empirical investigation of V-I trajectory based load signatures for non-intrusive load monitoring
【24h】

An empirical investigation of V-I trajectory based load signatures for non-intrusive load monitoring

机译：基于V-I轨迹的非侵入式负荷监测的轨迹的实证研究

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Choice of load signature or feature space is one of the most fundamental design choices for non-intrusive load monitoring or energy disaggregation problem. Electrical power quantities, harmonic load characteristics, canonical transient and steady-state waveforms are some of the typical choices of load signature or load signature basis for current research addressing appliance classification and prediction. This paper expands and evaluates appliance load signatures based on V-I trajectory — the mutual locus of instantaneous voltage and current waveforms — for precision and robustness of prediction in classification algorithms used to disaggregate residential overall energy use and predict constituent appliance profiles. We also demonstrate the use of variants of differential evolution as a novel strategy for selection of optimal load models in context of energy disaggregation. A publicly available benchmark dataset REDD is employed for evaluation purposes. Our experimental evaluations indicate that these load signatures, in conjunction with a number of popular classification algorithms, offer better or generally comparable overall precision of prediction, robustness and reliability against dynamic, noisy and highly similar load signatures with reference to electrical power quantities and harmonic content. Herein, wave-shape features are found to be an effective new basis of classification and prediction for semi-automated energy disaggregation and monitoring.
机译：负载签名或特征空间的选择是非侵入式负载监测或能量分类问题的最基本的设计选择之一。电力量，谐波荷载特性，规范瞬态和稳态波形是负载签名或负载签名基础的一些典型选择，用于当前研究寻址设备分类和预测。本文扩展和评估了基于V-I轨迹的器具负载签名 - 瞬时电压和电流波形的相互基因座 - 用于分类算法中预测的精度和鲁棒性，用于分解住宅整体能源使用和预测构成设备概况。我们还证明了使用差分演进的变体作为选择能量分解背景下选择最佳载荷模型的新策略。公开可用的基准数据集REDD用于评估目的。我们的实验评估表明，这些负载符号与许多流行的分类算法一起提供更好或通常可比的预测，鲁棒性和可靠性的整体精度，以及参考电力量和谐波含量的动态，嘈杂和高度相似的负载符号。这里，发现波形特征是半自动能量分解和监测的分类和预测的有效新的基础。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号