Big IoT data mining for real-time energy disaggregation in buildings

D.C. Mocanu, E. Mocanu, H.P. Nguyen, M. Gibescu, A. Liotta

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

14 Citaten (Scopus)
749 Downloads (Pure)

Samenvatting

In the smart grid context, the identification and prediction of building energy flexibility is a challenging open question, thus paving the way for new optimized behaviors from the demand side. At the same time, the latest smart meters developments allow us to monitor in real-time the power consumption level of the home appliances, aiming at a very accurate energy disaggregation. However, due to practical constraints is infeasible in the near future to attach smart meter devices on all home appliances, which is the problem addressed herein. We propose a hybrid approach, which combines sparse smart meters with machine learning methods. Using a subset of buildings equipped with subset of smart meters we can create a database on which we train two deep learning models, i.e. Factored Four-Way Conditional Restricted Boltzmann Machines (FFW-CRBMs) and Disjunctive FFW-CRBM. We show how our method may be used to accurately predict and identify the energy flexibility of buildings unequipped with smart meters, starting from their aggregated energy values. The proposed approach was validated on a real database, namely the Reference Energy Disaggregation Dataset. The results show that for the flexibility prediction problem solved here, Disjunctive FFW-CRBM outperforms the FFW-CRBMs approach, where for classification task their capabilities are comparable.
Originele taal-2Engels
TitelProceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016), 9-12 October 2016, Budapest, Hungary
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's3765-3769
Aantal pagina's5
ISBN van elektronische versie978-1-5090-1897-0
ISBN van geprinte versie978-1-5090-1898-7
DOI's
StatusGepubliceerd - 9 feb 2017

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  • Prijzen

    Student Travel Grant - IEEE SMC 2016

    Mocanu, Decebal C. (Ontvanger), 12 okt 2016

    Prijs: AndersBeurzenWetenschappelijk

    Citeer dit

    Mocanu, D. C., Mocanu, E., Nguyen, H. P., Gibescu, M., & Liotta, A. (2017). Big IoT data mining for real-time energy disaggregation in buildings. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016), 9-12 October 2016, Budapest, Hungary (blz. 3765-3769). [7844820] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SMC.2016.7844820