Independent component analysis, a new concept?
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
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FireFly: a cross-layer platform for real-time embedded wireless networks
Real-Time Systems
Support Vector Machines
Machine Learning: An Algorithmic Perspective
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ViridiScope: design and implementation of a fine grained power monitoring system for homes
Proceedings of the 11th international conference on Ubiquitous computing
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Contactless sensing of appliance state transitions through variations in electromagnetic fields
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
Disaggregated End-Use Energy Sensing for the Smart Grid
IEEE Pervasive Computing
Recognizing the use of portable electrical devices with hand-worn magnetic sensors
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Recognizing handheld electrical device usage with hand-worn coil of wire
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Editorial: Advanced computing for the built environment
Advanced Engineering Informatics
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Non-Intrusive Load Monitoring (NILM) has been studied for a few decades now as a method of disaggregating information about appliance level power consumption in a building from aggregate measurements of voltage and/or current obtained at a centralized location in the electrical system. When such information is provided to the electricity consumer as feedback, they can then take the necessary steps to modify their behavior and conserve electricity. Research has shown potential for savings of up to 20% through this kind of feedback. The training phase required to allow the algorithms to recognize appliances in the home at the beginning of a NILM setup is a big hindrance to wide adoption of the technique. One of the recent advances in this research area includes the addition of an Electro-Magnetic Field (EMF) sensor that measures the electric and magnetic field nearby an appliance to detect its operational state. This information, when coupled with the aggregate power consumption data for the home, can help to train a NILM system, which is a significant step forward in automating the training phase. This paper explores the theory behind the operation of the EMF sensor and discusses the feasibility of automating the training and classification process using these devices. A case study is presented, where magnetic field measurements of eight appliances are analyzed to determine the viability of using these signals alone to determine the type of appliance that the EMF sensor has been placed next to. Various dimensionality reduction techniques are applied to the collected data, and the resulting feature vectors are used to train a variety of common machine learning classifiers. A vector subspace obtained using Independent Component Analysis (ICA), along with a k-NN classifier, was found to perform best among the different alternatives explored. Possible reasons behind the findings are discussed and areas for further exploration are proposed.