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KDnuggets Home » News » 2013 » Jul » Competitions » Kaggle Belkin Energy Disaggregation Competition ( 13:n18 )

Kaggle Belkin Energy Disaggregation Competition


Use machine learning on EMI signatures and other data to understand what appliances are used as a step for providing personalized and cost-effective energy saving recommendations.



Belkin Energy Disaggregation CompetitionDisaggregate household energy consumption into individual appliances

Competition ends: Oct 30, 2013

Imagine an energy feedback system that displays not only your total power consumption, but also continuously shows real-time usage, broken down by electrical appliance. Such a system could provide personalized and cost-effective energy saving recommendations. For example, it could report, “Based on your usage patterns, you could save $215 per year by switching to a more efficient heating unit, which will pay for itself in 27 months.” The challenge in this scenario is to sense end-uses of energy to provide feedback at the fine-grained, appliance level.

… A more recent approach to estimate appliance usage is to examine the Electromagnetic Interference (EMI) that most consumer electronic appliances produce as identifying signatures. This EMI is measured using a special sensor built at the Ubicomp Lab at the University of Washington as part of Sidhant Gupta’s thesis work. The figure below shows an example of EMI captured from a home. The plot is in frequency domain and shows the signatures of various appliances.

EMI Plot

The presence or absence of such EMI signatures would ideally tell us when a particular appliance is in use. However, due to the large numbers of appliances in a home, the solution is not straightforward. Machine learning is required not only to make an inference about the appliance class given a particular signature, but probabilistic models are needed that take into account, for example, human appliance usage patterns (think using coffee machine and toaster in morning vs. lights in evening), weather patterns (very unlikely that AC came on during winters), and appliance electrical model. The signature of an appliance can also drift or vary over time due to operating conditions and the mode in which they are used (for instance, a washing machine has many modes). We encourage participants to review [4] to better understand the use of EMI for electrical appliance use detection and classification.

For more information and to participate

https://www.kaggle.com/c/belkin-energy-disaggregation-competition


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