Browsing by Author "Tsai, Tzung-Cheng"

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A Novel Search Scheme for Nonintrusive Load Monitoring Systems
(2012)
Lai, Yi-Sheng
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Chen, Yung-Chi
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Tsao, Shiao-Li
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Tsai, Tzung-Cheng
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資訊工程學系
A nonintrusive load monitoring (NILM) system analyzes the changes of voltage and current on an electrical circuit and can disaggregate the usage and power consumption of appliances on the same circuit loop according to different power consumption signatures of the appliances. An NILM system is regarded as one of the most important features for energy monitoring and management in a smart home and building. Advances in computing, communication and storage technologies further make an NILM system possible to accommodate a huge amount of power consumption signatures of appliances, and to detect appliances and/or appliance states through database search techniques. In this study, we propose a low-cost NILM system for a smart home and building to facilitate end users contributing the power consumption signatures of their appliances to a datacenter through confirmations or feedbacks of the NILM search. Based on those user contributed datasets, a new NILM search algorithm is then presented in this paper. To evaluate and compare the performance and accuracy of NILM search results by employing the proposed and existing approaches, an evaluation test-bed is established. The experimental results demonstrate that the proposed scheme outperforms the existing algorithms and can achieve 85% to 96% accuracy of appliance state detections.
Detecting Users' Behaviors based on Nonintrusive Load Monitoring Technologies
(2013)
Chen, Yung-Chi
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Chu, Chun-Mei
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Tsao, Shiao-Li
;
Tsai, Tzung-Cheng
;
資訊工程學系
Conventional user behavior detection relies on a large amount of sensors and expensive monitoring devices. Moreover, the systems are usually intrusive and may suffer from deployment problems. In this paper, we design and implement an energy management system (EMS) consisting of a non-intrusive load monitoring (NILM) meter, gateway, server and mobile device. The NILM meter provides a non-intrusive and low-cost solution to recognize the states of appliances and to disaggregate the energy consumption of appliances in a house/building. Based on the proposed EMS, we further implement a data mining scheme to detect users' behaviors based on the usage patterns of appliances. A prototype system verifies our design concept and the simulation results show that the detection accuracy of users' behaviors is more than 80% for most of the activities.