Big IoT data mining for real-time energy disaggregation in buildings (extended abstract)

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

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Abstract

In the smart grid context, the identification and prediction of building energy flexibility is a challenging open question. In this paper, we propose a hybrid approach to address this problem. It combines sparse smart meters with deep learning methods, e.g. Factored Four-Way Conditional Restricted Boltzmann Machines (FFW-CRBMs), 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.
Original languageEnglish
Title of host publicationBenelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning
EditorsW. Duijvesteijn, M. Pechenizkiy, G. Fletcher, V. Menkovski, E. Postma, J. Vanschoren, P. van der Putten
Pages173-174
Number of pages2
Publication statusPublished - 10 Jun 2017
EventAnnual machine learning conference of the Benelux (Benelearn 2017) - Eindhoven, Netherlands
Duration: 9 Jun 201710 Jun 2017
http://wwwis.win.tue.nl/~benelearn2017/

Conference

ConferenceAnnual machine learning conference of the Benelux (Benelearn 2017)
Abbreviated titleBenelearn 2017
CountryNetherlands
CityEindhoven
Period9/06/1710/06/17
Internet address

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    Mocanu, D. C., Mocanu, E., Nguyen, H. P., Gibescu, M., & Liotta, A. (2017). Big IoT data mining for real-time energy disaggregation in buildings (extended abstract). In W. Duijvesteijn, M. Pechenizkiy, G. Fletcher, V. Menkovski, E. Postma, J. Vanschoren, & P. van der Putten (Eds.), Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning (pp. 173-174)