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NRB is an IT services company based in Li`ege. A part of their business revolves
around the management of changes in programs and infrastructures at the request of
their clients. Any applied change may potentially provoke unforeseen incidents. To
avoid this, each change is linked with a risk level that packs the probability and the
level of impact of potential incidents. This way, limited resources can be adequately
allocated to sensitive changes. Risk levels are assessed by human, based on several
factors.
However, those assessments are sometimes inaccurate, and lead to incidents or bad
resources allocation. Thus, this thesis is an attempt at automating the assessment
of risks levels. This problem reduces to a supervised classification problem that may
be linked to the field of defects prediction with contextual parameters.
Before conducting any experiment, the data is explored in details. Then, to
classify changes, we try several types of algorithms, including boosting ensembles,
support vector machines, neural networks, ... Transfer learning for NLP is also used
with Google’s BERT. The skewness of the labels leads to the exploration of imbalanced learning solutions. This includes resampling, classes weighting and models
selection (like boosting). To assess the performances of the classifiers we develop a
custom metric that is inspired from the field of cost sensitive learning.
A comparison with human performances shows the inability of any developed
classifier to correctly detect classes. To improve this classifier, We advocate to explore in more details the field of imbalanced learning. We also advocate to use
cross-validation techniques applied to temporal data. The development of a custom language model learned on a corpus of texts from NRB may also improve the
performances.

defect prediction --- machine learning --- natural language processing --- NRB --- Ingénierie, informatique & technologie > Sciences informatiques

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Automation is happening in all aspects of our daily lives. This work aims at improving the efficiency of NRB Service Desk thanks to machine learning and natural language processing by automating the routing of incidents tickets. This is mainly done by analysing the ticket textual content.

Machine learning classifiers is compared after putting the data into the right shape. The logistic regression performed the best followed by SVM. In the end, a short study with deep learning is carried.

Machine learning --- Natural language processing --- python --- automation --- service desk --- Ingénierie, informatique & technologie > Sciences informatiques

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Renewable energies are challenging to forecast due to their intermittence. However, it is crucial for the energy transition to predict accurately what is going to be produced at different temporal resolution (short, mid or long term) to integrate them in the network. In this work, we investigate the short term horizon. We work in the practical setting of the day-ahead forecast for wind farms. The aim of this work is twofold: to help the transmission system operator (TSO) in its task of balancing the network and the market participants of the day-ahead spot market. Both tasks require to know what is going to be produced for the next day. In this work, we will try new Artificial Intelligence (i.e. AI) models for wind energy forecasting. We explore state-of-the-art Machine Learning and Deep Learning models like Random Forest, Extra Trees, Recurrent Neural Network (i.e. RNN) and Transformers. We also investigate new RNN cells (e.g. BRC, nBRC and hybrid). We create original architectures of RNNs and Transformers. To compare the models and assess the results, we use two datasets: the ORES and the Gefcom2014 dataset. The first dataset is built from ORES recording productions of wind farms located in Belgium and weather data produced by the MAR (Modèle Atmosphérique Régional) developed at the University of Liège. The second dataset is often used in the scientific community. Then, we perform a deep analysis of the results given by the best models on both datasets. Additionally, we provide perspectives of improvement and we discuss other interesting techniques to investigate further.

Deep Learning --- Machine Learning --- Energy --- Artificial Intelligence --- Energy Forecasting --- TSO --- Day-ahead forecast --- Renewable Energy --- Ingénierie, informatique & technologie > Sciences informatiques