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Loubna Benabbou

Independent visiting researcher - UQAR
Supervisor

Publications

Deep Learning Model for Multi-Step Ahead Prediction of Solar Irradiance: Case of Study of Morocco
Saad Benbrahim
Loubna Benabbou
Ismail Belhaj
Abdelali Djdiaa
Hicham Bouzekri
Abdelaziz Berrado
Accurate solar irradiance forecasting is crucial for managing energy generation and consumption in the rapidly evolving landscape of renewab… (see more)le energy. It enables renewable energy operators to make informed decisions and maximize their output. This study employs deep learning-based forecasting models to predict the Global Horizontal Irradiance (GHI) of the R&D platform situated in Ouarzazate, Morocco. A sensitivity analysis was conducted on multiple scenarios for a one day-ahead horizon. Moreover, a forecasting technique that encompasses numerous horizons, ranging from one day to three days in advance, was evaluated. The study's findings suggest that the encoder-decoder model we proposed exhibited superior performance compared to the other models tested and produced dependable predictions.
Towards an Effective Electrical Market Design: Identifying and Defining Key Criteria for Decision-Making
Souhaila Chiguer
Loubna Benabbou
Ismail Belhaj
Abdelali Djdiaa
Hicham Bouzekri
Abdelaziz Berrado
In our changing energy landscape, electricity is taking a major role in achieving decarbonization goals. Electricity can be a clean and effi… (see more)cient source of energy, and it is well-suited to help countries meet their climate goals. However, the electrical market is complex and constantly evolving, and it is important to carefully choose the design elements of the market to ensure that it is meeting its objectives. In this context, evaluating an electrical market's effectiveness requires a multifaceted approach that takes into account a range of elements, from environmental impact to economic viability. This paper provides an overview of several evaluation methods for different objectives to finally select the key criteria to consider in assisting decision-makers, regulators, and stakeholders in developing an electricity market that is not only effective but also reliable and sustainable.
Predicting Solar PV Output Based on Hybrid Deep Learning and Physical
Models: Case Study of Morocco
Samira Abousaid
Loubna Benabbou
Ismail Belhaj
Abdelaziz Berrado
Hicham Bouzekri
Improving *day-ahead* Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context
Oussama Boussif
Ghait Boukachab
Dan Assouline
Stefano Massaroli
Tianle Yuan
Loubna Benabbou
Solar power harbors immense potential in mitigating climate change by substantially reducing CO…
What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?
Oussama Boussif
Ghait Boukachab
Dan Assouline
Stefano Massaroli
Tianle Yuan
Loubna Benabbou
The global integration of solar power into the electrical grid could have a crucial impact on climate change mitigation, yet poses a challen… (see more)ge due to solar irradiance variability. We present a deep learning architecture which uses spatio-temporal context from satellite data for highly accurate day-ahead time-series forecasting, in particular Global Horizontal Irradiance (GHI). We provide a multi-quantile variant which outputs a prediction interval for each time-step, serving as a measure of forecasting uncertainty. In addition, we suggest a testing scheme that separates easy and difficult scenarios, which appears useful to evaluate model performance in varying cloud conditions. Our approach exhibits robust performance in solar irradiance forecasting, including zero-shot generalization tests at unobserved solar stations, and holds great promise in promoting the effective use of solar power and the resulting reduction of CO
What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?
Oussama Boussif
Ghait Boukachab
Dan Assouline
Stefano Massaroli
Tianle Yuan
Loubna Benabbou