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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Villeneuve, Y., Séguin, S., Anjos, M. F., & Demeester, K. (2026). Managing short-term hydropower production with a multi-agent reinforcement learning model. (Technical Report n° G-2026-37). External link
Daadaa, M., Séguin, S., Anjos, M. F., & Demeester, K. (2023). Quantifying the impact of scenario tree generation and reduction methods on the solution of the short-term hydroscheduling problem. Energy Systems, 30 pages. External link
Daadaa, M., Séguin, S., Anjos, M. F., & Demeester, K. (2023). Optimizing strategies for short-term hydropower scheduling using a blackbox optimization framework. (Technical Report n° G-2023-38). External link
Daadaa, M., Séguin, S., Anjos, M. F., & Demeester, K. (2022). Quantifying the impact of scenario tree generation methods on the solution of the short-term hydroscheduling problem. (Technical Report n° G-2022-47). External link
Daadaa, M., Séguin, S., Demeester, K., & Anjos, M. F. (2021). An optimization model to maximize energy generation in short-term hydropower unit commitment using efficiency points. International Journal of Electrical Power and Energy Systems, 125, 8 pages. External link
Daadaa, M., Séguin, S., Anjos, M. F., & Demeester, K. (2019). A linear mixed-integer formulation of the short-term hydropower problem. (Technical Report n° G-2019-63). External link
Demeester, K. (2017). Méthodes numériques appliquées à la programmation dynamique stochastique pour la gestion d'un système hydroélectrique [Master's thesis, École Polytechnique de Montréal]. Available
Martel, J.-L., Demeester, K., Brissette, F., Poulin, A., & Arsenault, R. (2017). HMETS-A Simple and Efficient Hydrology Model for Teaching Hydrological Modelling, Flow Forecasting and Climate Change Impacts. International Journal of Engineering Education, 33(4), 1307-1316. External link
Côté, P., Demeester, K., Orban, D., & Towhidi, M. (2017). Numerical methods for stochastic dynamic programming with application to hydropower optimization. (Technical Report n° G-2017-64). External link
