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2010 NIPS NeurIPS 2010

Energy Disaggregation via Discriminative Sparse Coding

Abstract

Energy disaggregation is the task of taking a whole-home energy signal and separating it into its component appliances. Studies have shown that having device-level energy information can cause users to conserve significant amounts of energy, but current electricity meters only report whole-home data. Thus, developing algorithmic methods for disaggregation presents a key technical challenge in the effort to maximize energy conservation. In this paper, we examine a large scale energy disaggregation task, and apply a novel extension of sparse coding to this problem. In particular, we develop a method, based upon structured prediction, for discriminatively training sparse coding algorithms specifically to maximize disaggregation performance. We show that this significantly improves the performance of sparse coding algorithms on the energy task and illustrate how these disaggregation results can provide useful information about energy usage.

🧭 Keyword Pioneer — energy disaggregation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
📈 Trend Setter — Sparse Coding
🐣 Hot Topic Early Bird — feature learning

Authors

J. Z. Kolter , Siddharth Batra , Andrew Y. Ng

Topics

Machine Learning > Core Methods > Classification Machine Learning > Core Methods > Representation Learning Machine Learning > Optimization & Theory > Optimization Machine Learning > Application Areas > Domain Adaptation Machine Learning > Learning Types > Supervised Learning Machine Learning > Core Methods > Feature Learning Machine Learning > Core Methods > Structured Prediction Machine Learning > Core Methods > Sparse Coding

Keywords

feature learning signal separation feature extraction structured prediction sparse coding energy disaggregation appliance classification
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