Piga, Dario and Cominola, Andrea and Giuliani, Matteo and Castelletti, Andrea and Rizzoli, Andrea Emilio (2016) Sparse Optimization for Automated Energy End Use Disaggregation. IEEE Transactions on Control Systems Technology, 24 (3). pp. 1044-1051. ISSN 1063-6536
Full text not available from this repository.Abstract
Retrieving the household electricity consumption at individual appliance level is an essential requirement to assess the contribution of different end uses to the total household consumption, and thus to design energy saving policies and user-tailored feedback for reducing household electricity usage. This has led to the development of nonintrusive appliance load monitoring (NIALM), or energy disaggregation, algorithms, which aim to decompose the aggregate energy consumption data collected from a single measurement point into device-level consumption estimations. Existing NIALM algorithms are able to provide accurate estimate of the fraction of energy consumed by each appliance. Yet, in the authors' experience, they provide poor performance in reconstructing the power consumption trajectories overtime. In this brief, a new NIALM algorithm is presented, which, besides providing very accurate estimates of the aggregated consumption by appliance, also accurately characterizes the appliance power consumption profiles overtime. The proposed algorithm is based on the assumption that the unknown appliance power consumption profiles are piecewise constant overtime (as it is typical for power use patterns of household appliances) and it exploits the information on the time-of-day probability in which a specific appliance might be used. The disaggregation problem is formulated as a least-square error minimization problem, with an additional (convex) penalty term aiming at enforcing the disaggregate signals to be piecewise constant overtime. Testing on household electricity data available in the literature is reported
Item Type: | Scientific journal article, Newspaper article or Magazine article |
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Subjects: | Engineering > Civil engineering Computer sciences Computer sciences > Artificial intelligence Computer sciences > Artificial intelligence > Machine learning |
Department/unit: | Dipartimento tecnologie innovative > Istituto Dalle Molle di studi sull’intelligenza artificiale USI-SUPSI |
Depositing User: | Andrea Emilio Rizzoli |
Date Deposited: | 09 Mar 2018 14:47 |
Last Modified: | 09 Mar 2018 14:47 |
URI: | http://repository.supsi.ch/id/eprint/9367 |
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