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Fakhreddine S. Oueslati
Researcher at Carthage College
Publications - 27
Citations - 498
Fakhreddine S. Oueslati is an academic researcher from Carthage College. The author has contributed to research in topics: Heat transfer & Natural convection. The author has an hindex of 8, co-authored 26 publications receiving 179 citations. Previous affiliations of Fakhreddine S. Oueslati include Tianjin University of Commerce & École Normale Supérieure.
Papers
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Journal ArticleDOI
A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting
Mohamed Massaoudi,Mohamed Massaoudi,Shady S. Refaat,Ines Chihi,Mohamed Trabelsi,Fakhreddine S. Oueslati,Haitham Abu-Rub +6 more
TL;DR: A novel stacking ensemble-based algorithm is proposed that copes with the stochastic variations of the load demand using a stacked generalization approach and is validated using two datasets from different locations: Malaysia and New England.
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An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting
Mohamed Massaoudi,Ines Chihi,Lilia Sidhom,Mohamed Trabelsi,Shady S. Refaat,Haitham Abu-Rub,Fakhreddine S. Oueslati +6 more
TL;DR: In this paper, an effective Photovoltaic (PV) power forecasting (PVPF) technique based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN) with Long Short-Term Memory (LSTM) model was proposed.
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Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects
TL;DR: In this article, the authors provide a thorough review from a broad perspective on the state-of-the-art advances of DL in smart grid systems, including federated learning, edge intelligence, and distributed computing.
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Heterogeneous nanofluids: natural convection heat transfer enhancement.
TL;DR: The heat transfer in natural convection increases with nanoparticle concentration but remains less than the enhancement previously underlined in forced convection case, while the induced nanofluid heterogeneity showed a significant heat transfer modification.
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Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review
TL;DR: In this article, a review article taxonomically dives into the nitty-gritty of the mainstream DL-based PVPF methods while showcasing their strengths and weaknesses, and concludes with some research gaps and hints about future challenges and research directions in driving the further success of DL techniques to power forecasting.