Chapter
Apr 26, 2012
Learning Systems for Electric Consumption of Buildings
Authors: Mario Berges [email protected], Ethan Goldman [email protected], H. Scott Matthews [email protected], and Lucio Soibelman [email protected]Author Affiliations
Publication: Computing in Civil Engineering (2009)
Abstract
Individual appliances' electricity consumption is automatically disaggregated from a single custom metering system on the main feed to an occupied residential building. A data acquisition system samples voltage and current at 100 kHz, then calculates real and reactive power, harmonics, and other features at 20Hz. A probabilistic eventdetector using the generalized likelihood ratio (GLR) matches human-labeled events to the time-series of features. Machine-learning classification was most successful with the 1-nearest-neighbor algorithm, correctly identifying 90% of the laboratory-generated training events and 79% of validation examples. The challenge of obtaining adequate training data for the real-world home leads to the development of the Wire Spy, a wirelessly-networked event detector with an inductive sensor which clamps to the cable of an appliance.
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© 2009 American Society of Civil Engineers.
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Published online: Apr 26, 2012
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Civil and Environmental Engineering Department, Carnegie Mellon University. E-mail: [email protected]
Civil and Environmental Engineering Department, Carnegie Mellon University. E-mail: [email protected]
Civil and Environmental Engineering Department, Carnegie Mellon University. E-mail: [email protected]
Civil and Environmental Engineering Department, Carnegie Mellon University. E-mail: [email protected]
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