Machine Learning
A conjoint pattern recognition approach to nonintrusive load monitoring
A conjoint pattern recognition approach to nonintrusive load monitoring
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Discrete Wavelet Soft Threshold Denoise Processing for ECG Signal
ICICTA '10 Proceedings of the 2010 International Conference on Intelligent Computation Technology and Automation - Volume 02
Extracting features from an electrical signal of a non-intrusive load monitoring system
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
On denoising and best signal representation
IEEE Transactions on Information Theory
De-noising by soft-thresholding
IEEE Transactions on Information Theory
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Electrical load disaggregation for end-use recognition in the smart home has become an area of study of its own right. The most well-known examples are energy monitoring, health care applications, in-home activity modeling, and home automation. Real-time energy-use analysis for whole-home approaches needs to understand where and when the electrical loads are spent. Studies have shown that individual loads can be detected (and disaggregated) from sampling the power at one single point (e.g. the electric service entrance for the house) using a non-intrusive load monitoring (NILM) approach. In this paper, we focus on the feature extraction and pattern recognition tasks for non-intrusive residential electrical consumption traces. In particular, we develop an algorithm capable of determining the step-changes in signals that occur whenever a device is turned on or off, and which allows for the definition of a unique signature (ID) for each device. This algorithm makes use of features extracted from active and reactive powers and power factor. The classification task is carried out by Support Vector Machines and 5-Nearest Neighbors methods. The results illustrate the effectiveness of the proposed signature for distinguishing the different loads.