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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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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 language	English
Title of host publication	Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning
Editors	W. Duijvesteijn, M. Pechenizkiy, G. Fletcher, V. Menkovski, E. Postma, J. Vanschoren, P. van der Putten
Pages	173-174
Number of pages	2
Publication status	Published - 10 Jun 2017
Event	Annual machine learning conference of the Benelux (Benelearn 2017) - Eindhoven, Netherlands Duration: 9 Jun 2017 → 10 Jun 2017 http://wwwis.win.tue.nl/~benelearn2017/

Conference

Conference	Annual machine learning conference of the Benelux (Benelearn 2017)
Abbreviated title	Benelearn 2017
Country/Territory	Netherlands
City	Eindhoven
Period	9/06/17 → 10/06/17
Internet address	http://wwwis.win.tue.nl/~benelearn2017/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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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)

@inproceedings{7b964b9f4271417382d1296655bc5406,

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

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.",

author = "D.C. Mocanu and E. Mocanu and H.P. Nguyen and M. Gibescu and A. Liotta",

year = "2017",

month = jun,

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language = "English",

pages = "173--174",

editor = "\{ Duijvesteijn\}, W. and Pechenizkiy, \{M. \} and G. Fletcher and V. Menkovski and E. Postma and J. Vanschoren and \{van der Putten\}, P.",

booktitle = "Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning",

note = "Annual machine learning conference of the Benelux (Benelearn 2017), Benelearn 2017 ; Conference date: 09-06-2017 Through 10-06-2017",

url = "http://wwwis.win.tue.nl/\textasciitilde{}benelearn2017/",

}

Mocanu, DC, Mocanu, E, Nguyen, HP, 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, Annual machine learning conference of the Benelux (Benelearn 2017), Eindhoven, Netherlands, 9/06/17.

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Big IoT data mining for real-time energy disaggregation in buildings (extended abstract)

AU - Mocanu, D.C.

AU - Mocanu, E.

AU - Nguyen, H.P.

AU - Gibescu, M.

AU - Liotta, A.

PY - 2017/6/10

Y1 - 2017/6/10

N2 - 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.

AB - 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.

M3 - Conference contribution

SP - 173

EP - 174

BT - Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning

A2 - Duijvesteijn, W.

A2 - Pechenizkiy, M.

A2 - Fletcher, G.

A2 - Menkovski, V.

A2 - Postma, E.

A2 - Vanschoren, J.

A2 - van der Putten, P.

T2 - Annual machine learning conference of the Benelux (Benelearn 2017)

Y2 - 9 June 2017 through 10 June 2017

ER -

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

Abstract

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UN SDGs

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