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Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
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Master's Thesis : Comparison of probabilistic forecasting deep learning models in the context of renewable energy production

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Stassen, Théo ULiège
Promotor(s) : Ernst, Damien ULiège
Date of defense : 7-Sep-2020/9-Sep-2020 • Permalink : http://hdl.handle.net/2268.2/10466
Details
Title : Master's Thesis : Comparison of probabilistic forecasting deep learning models in the context of renewable energy production
Translated title : [fr] Comparison de modèles de prédiction probabilistique utilisant l'apprentissage profond dans le contexte de la production d'énergie renouvelable
Author : Stassen, Théo ULiège
Date of defense  : 7-Sep-2020/9-Sep-2020
Advisor(s) : Ernst, Damien ULiège
Committee's member(s) : Gemine, Quentin ULiège
Mathieu, Sébastien ULiège
Language : English
Number of pages : 99
Keywords : [en] artificial intelligence
[en] machine learning
[en] forecasting
[en] time series forecasting
[en] probabilistic forecasting
[en] deep learning
[en] forecasting models
[en] renewable energy
[en] renewable energy production
[en] model comparison
Discipline(s) : Engineering, computing & technology > Computer science
Target public : Researchers
Professionals of domain
Student
General public
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] This master thesis subject addresses the question of what is the best forecasting method to implement in the context of the prediction of renewable energy production, to protect assets from oversupply. The growing scientific field of Deep Learning has a great potential to be exploited to achieve this goal. This works is composed of different parts. First part introduces the goal that we want to be pursued. The second part interrogates what what are the tools needed to accomplish the goal and defines the context on which the comparison will be performed. The third part is a comparison of the models considering a default forecasting goal . The fourth part is a discussion on what might be the most relevant metric considering the main goal. From this we define two metrics, Coverage and MASE and we finally perform in fifth part a comparison using metrics and loss that have been introduced .
The answer to the question of what is the better forecasting model in the defined context between all the tested models, the model that provides the better results, in terms of Coverage and MASE, is definitely the model MQCNN, which outperforms for the two metrics considered all the other presented models. MQCNN model is followed by MQRNN, DeepAr and SimpleFeedForward.


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Access Master_Thesis_Final(11).pdf
Description: Master Thesis document
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Description: Master Thesis Summary
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Author

  • Stassen, Théo ULiège Université de Liège > Master ingé. civ. info., à fin.

Promotor(s)

Committee's member(s)

  • Gemine, Quentin ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids
    ORBi View his publications on ORBi
  • Mathieu, Sébastien ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids
    ORBi View his publications on ORBi
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