Applying power meters for appliance recognition on the electric panel

Lin, Gu-yuan; Lee, Shih-chiang; Hsu, Jane Yung-jen; Jih, Wan-rong

doi:10.1109/iciea.2010.5515385

Cited by 25 publications

(9 citation statements)

References 11 publications

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“…Identifying their states was mainly described stochastically or predicted with data-driven methods. The former approaches use Bayesian networks [244,245] and hierarchical clustering models [246]. The latter use two different machine-learning-based algorithms: HMMs [239,[247][248][249] and NNs [250][251][252].…”

Section: Appliance Usementioning

confidence: 99%

Modeling occupant behavior in buildings

Carlucci

Simone

Firth

et al. 2020

Building and Environment

155

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In the last four decades several methods have been used to model occupants' presence and actions (OPA) in buildings according to different purposes, available computational power, and technical solutions. This study reviews approaches, methods and key findings related to OPA modeling in buildings. An extensive database of related research documents is systematically constructed, and, using bibliometric analysis techniques, the scientific production and landscape are described. The initial literature screening identified more than 750 studies, out of which 278 publications were selected. They provide an overarching view of the development of OPA modeling methods. The research field has evolved from longitudinal collaborative efforts since the late 1970s and, so far, covers diverse building typologies mostly concentrated in a few climate zones. The modeling approaches in the selected literature are grouped into three categories (rule-based models, stochastic OPA modeling, and data-driven methods) for modeling occupancy-related target functions and a set of occupants' actions (window, solar shading, electric lighting, thermostat adjustment, clothing adjustment and appliance use). The explanatory modeling is conventionally based on the model-based paradigm where occupant behavior is assumed to be stochastic, while the Revised Manuscript with No Changes MarkedClick here to view linked References data-driven paradigm has found wide applications for the predictive modeling of OPA, applicable to control systems. The lack of established standard evaluation protocols was identified as a scientifically important yet rarely addressed research question. In addition, machine learning and deep learning are emerging in recent years as promising methods to address OPA modeling in real-world applications.

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Section: Appliance Usementioning

confidence: 99%

Modeling occupant behavior in buildings

Carlucci

Simone

Firth

et al. 2020

Building and Environment

155

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“…Appliance recognition [17][18][19] provides alternative way to discover human behavior indirectly. Because of the relevance between activity and appliance usage Lee et al present an energy conservation framework [20] consisting of appliances recognition, activity-appliances model, unattended appliances detection, and energy conservation service.…”

Section: B Appliance and Activity Recognitionmentioning

confidence: 99%

Integration of IoT Energy Management System with Appliance and Activity Recognition

Lai

Yang

et al. 2012

2012 IEEE International Conference on Green Computing and Communications

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The Internet of Things (IoT) extends and expands the range of the internet by interconnecting Internet and end device networks. As the raising of awareness about IoT, more and more application may applied for various areas. Especially, how to develop intelligent systems for energy saving becomes a new challenge in all circles.This research integrated appliance and activity recognition mechanism for IoT energy management system. It presented a management service layer for the recognition of current household appliances, which not only establishes communication services among various appliances, and deduced human activity conducted for context data using Naive Bayes from the electric appliances in use and the variation of its states. Finally, the proposed system can automatically achieve energy management by controlling electric appliances.

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“…For the final step, various classification methods have been used, including support vector machines (SVM) [14], neural networks [15,16], non-negative tensor factorization [17], k-means [18], decision trees (DT) [19], optimization-based methods [20], and Graph Signal Processing (GSP) [7].…”

Section: Introductionmentioning

confidence: 99%

“…Based on the inference method applied, NILM algorithms can be supervised, semi-supervised or unsupervised. In supervised NILM (e.g., [14,15,19]), a labeled dataset (e.g., a diary of appliance usage or plug-based appliance monitors) is used to train machine learning models, which are then used to disaggregate unseen, test data. Semi-supervised NILM methods (e.g., [7]) use a small amount of labeled data and many unlabeled samples to train the models.…”

Section: Introductionmentioning

confidence: 99%

Building a Graph Signal Processing Model Using Dynamic Time Warping for Load Disaggregation

Stanković

2020

Sensors

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Building on recent unsupervised Non-intrusive load monitoring (NILM) algorithms that use graph Laplacian regularization (GLR) and achieve state-of-the-art performance, in this paper, we propose a novel unsupervised approach to design an underlying graph to model the correlation within time-series smart meter measurements. We propose a variable-length data segmentation approach to extract potential events, assign all measurements associated with an identified event to each graph node, employ dynamic time warping to define the adjacency matrix of the graph, and propose a robust cluster labeling approach. Our simulation results on four different datasets show up to 10% improvement in classification performance over competing approaches.

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Applying power meters for appliance recognition on the electric panel

Cited by 25 publications

References 11 publications

Modeling occupant behavior in buildings

Modeling occupant behavior in buildings

Integration of IoT Energy Management System with Appliance and Activity Recognition

Building a Graph Signal Processing Model Using Dynamic Time Warping for Load Disaggregation

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