Deep Learning Approach based on FCRBM for Optimization of Electric Energy Production

Chaimaa Fouhad, Mohamed El Khaili, Mohammed Qbadou

2021

Abstract

This correspondence features Deep Learning's commitment to the electrical energy sector. An overview of the concept will highlight the commitment of this innovation in enhancing the creation of electrical energy, prior to setting out on the decision of the model from which we will begin. Latter's choice is made after comparative studies between the different models used by other authors in their previous publications on the subject. This investigation in the end drove us to embrace the Factored Conditional Restricted Boltzmann Machine "FCRBM" model as a model that was viewed as powerful in correlation with others in a similar class. The FCRBM is a five-layer model, including three layers of the CRBM strategy, to which two new added substance layers have been added to work on the exactness and give new usefulness and functionality.

Paper Citation

in Harvard Style

Fouhad C., El Khaili M. and Qbadou M. (2021). Deep Learning Approach based on FCRBM for Optimization of Electric Energy Production. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 345-349. DOI: 10.5220/0010733900003101

in Bibtex Style

@conference{bml21,
author={Chaimaa Fouhad and Mohamed El Khaili and Mohammed Qbadou},
title={Deep Learning Approach based on FCRBM for Optimization of Electric Energy Production},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={345-349},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010733900003101},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - Deep Learning Approach based on FCRBM for Optimization of Electric Energy Production
SN - 978-989-758-559-3
AU - Fouhad C.
AU - El Khaili M.
AU - Qbadou M.
PY - 2021
SP - 345
EP - 349
DO - 10.5220/0010733900003101