通过有限混合物模型聚合使用加性时间序列建模进行非侵入式负荷监测

3区 计算机科学 Q1 Computer Science
Soudabeh Tabarsaii, Manar Amayri, Nizar Bouguila, Ursula Eicker
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引用次数: 0

摘要

能量分解或非侵入式负荷监测(NILM)涉及不同的方法,目的是在综合功率信号的情况下,区分各个电器的贡献。本文提出并研究了有限广义高斯混合物和有限伽马混合物在能量分解中的应用。该过程包括使用矩量法匹配对两个广义高斯随机变量(RV)之和的分布进行近似,以及对两个伽马随机变量之和的分布进行近似。通过采用这种方法,可以获得每种家电消费组合的概率分布,从而从汇总数据中预测和分解出具体的设备数据。此外,为了使模型更加实用,我们还提出了一个深度版本,我们称之为 DNN-Mixture,它是一个级联模型,由深度神经网络和每个建议的混合模型组合而成。作为广泛评估过程的一部分,我们在三个不同的数据集上应用了所提出的模型,这些数据集来自不同的地理位置,具有不同的采样率。结果表明,与高斯混合模型和其他广泛使用的方法相比,所提出的模型更具优势。为了研究我们的模型在具有挑战性的无监督环境中的适用性,我们在未标注数据的不可见房屋上对其进行了测试。测试结果证明了所提出方法的可扩展性和鲁棒性。最后,对级联模型与现有技术的对比评估表明,级联模型受益于神经网络和有限混合物的优点,可以产生与 RNN 相媲美的结果,而不会受到其固有缺点的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Non intrusive load monitoring using additive time series modeling via finite mixture models aggregation

Non intrusive load monitoring using additive time series modeling via finite mixture models aggregation

Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), involves different methods aiming to distinguish the individual contribution of appliances, given the aggregated power signal. In this paper, the application of finite Generalized Gaussian and finite Gamma mixtures in energy disaggregation is proposed and investigated. The procedure includes approximation of the distribution of the sum of two Generalized Gaussian random variables (RVs) and the approximation of the distribution of the sum of two Gamma RVs using Method-of-Moments matching. By adopting this procedure, the probability distribution of each combination of appliances consumption is acquired to predict and disaggregate the specific device data from the aggregated data. Moreover, to make the models more practical we propose a deep version, that we call DNN-Mixture, as a cascade model, which is a combination of a deep neural network and each of the proposed mixture models. As part of our extensive evaluation process, we apply the proposed models on three different datasets, from different geographical locations, that had different sampling rates. The results indicate the superiority of proposed models as compared to the Gaussian mixture model and other widely used approaches. In order to investigate the applicability of our models in challenging unsupervised settings, we tested them on unseen houses with unlabeled data. The outcomes proved the extensibility and robustness of the proposed approach. Finally, the evaluation of the cascade model against the state of the art shows that by benefiting from the advantages of both neural networks and finite mixtures, cascade model can produce promising and competing results with RNN without suffering from its inherent disadvantages.

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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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