Making large-scale support vector machine learning practical
Advances in kernel methods
A conjoint pattern recognition approach to nonintrusive load monitoring
A conjoint pattern recognition approach to nonintrusive load monitoring
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
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Electrical load disambiguation for end-use recognition in the residential sector has become an area of study of its own right. Several works have shown that individual loads can be detected (and separated) from sampling of the power at a single point (e.g. the electrical service entrance for the house) using a non-intrusive load monitoring (NILM) approach. This work presents the development of an algorithm for electrical feature extraction and pattern recognition, capable of determining the individual consumption of each device from the aggregate electric signal of the home. Namely, the idea consists of analyzing the electrical signal and identifying the unique patterns that occur whenever a device is turned on or off by applying signal processing techniques. We further describe our technique for distinguishing loads by matching different signal parameters (step-changes in active and reactive powers and power factor) to known patterns. Computational experiments show the effectiveness of the proposed approach.