(Big) data analytics in smart grids

E. Mocanu, H.P. Nguyen, M. Gibescu

Onderzoeksoutput: Bijdrage aan congresPoster

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Prediction of building energy consumption is a fundamental problem in the smart grid context. Unprecedented high volumes of data and information are available with the upward growth of the smart metering infrastructure. Therefore, we develop two deep learning methods. Firstly, the demand forecasting problem was solved at low aggregation levels (i.e. 1900 buildings) using factored conditional restricted Boltzmann machine. Secondly, we developed an unsupervised energy prediction method using reinforcement cross-building transfer able to accurately estimate the energy based on the information available in the neighborhood. Both methods have been successfully validated on real-world databases.
Originele taal-2Engels
StatusGepubliceerd - 29 jun. 2016
EvenementEuropean Data Forum 2016 (EDF 2016), June 29-30, 2016, Eindhoven, The Netherlands - Eindhoven, Nederland
Duur: 29 jun. 201630 jun. 2016


CongresEuropean Data Forum 2016 (EDF 2016), June 29-30, 2016, Eindhoven, The Netherlands
Verkorte titelEDF 2016
Internet adres


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