[HTML][HTML] Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting
In order to enable the transition towards renewable energy sources, probabilistic energy
forecasting is of critical importance for incorporating volatile power sources such as solar …
forecasting is of critical importance for incorporating volatile power sources such as solar …
[HTML][HTML] SkyGPT: Probabilistic ultra-short-term solar forecasting using synthetic sky images from physics-constrained VideoGPT
The variability of solar photovoltaic (PV) power output, driven by rapidly changing cloud
dynamics, hinders the transition to reliable renewable energy systems. Information on future …
dynamics, hinders the transition to reliable renewable energy systems. Information on future …
One-class machine learning classifiers-based multivariate feature extraction for grid-connected PV systems monitoring under irradiance variations
Z Yahyaoui, M Hajji, M Mansouri, K Bouzrara - Sustainability, 2023 - mdpi.com
In recent years, photovoltaic (PV) energy production has witnessed overwhelming growth,
which has inspired the search for more effective operations. Nevertheless, different PV faults …
which has inspired the search for more effective operations. Nevertheless, different PV faults …
Solar irradiance forecasting with transformer model
Solar energy is one of the most popular sources of renewable energy today. It is therefore
essential to be able to predict solar power generation and adapt energy needs to these …
essential to be able to predict solar power generation and adapt energy needs to these …
[HTML][HTML] Deep recurrent neural networks based Bayesian optimization for fault diagnosis of uncertain GCPV systems depending on outdoor condition variation
Y Bouazzi, Z Yahyaoui, M Hajji - Alexandria Engineering Journal, 2024 - Elsevier
Energy generated from renewable sources is exposed to extremely dynamic variations in
climatic conditions as well as uncertainties (current/voltage variability, noise, measurement …
climatic conditions as well as uncertainties (current/voltage variability, noise, measurement …
Forecasting of Solar Irradiance and Power in Uncertain Photovoltaic Systems Using BiLSTM and Bayesian Optimization
M Marweni, Z Yahyaoui, S Chaabani, M Hajji… - Arabian Journal for …, 2024 - Springer
The dynamic and intermittent nature of solar energy presents significant challenges for its
stable integration into current energy systems. Moreover, photovoltaic (PV) solar power …
stable integration into current energy systems. Moreover, photovoltaic (PV) solar power …
Photovoltaic Power Forecasting Using Multiscale-Model-Based Machine Learning Techniques
M Marweni, M Hajji, M Mansouri, MF Mimouni - Energies, 2023 - mdpi.com
The majority of energy sources being used today are traditional types. These sources are
limited in nature and quantity. Additionally, they are continuously diminishing as global …
limited in nature and quantity. Additionally, they are continuously diminishing as global …
SkyGPT: Probabilistic Short-term Solar Forecasting Using Synthetic Sky Videos from Physics-constrained VideoGPT
In recent years, deep learning-based solar forecasting using all-sky images has emerged as
a promising approach for alleviating uncertainty in PV power generation. However, the …
a promising approach for alleviating uncertainty in PV power generation. However, the …
Improving* day-ahead* Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context
Solar power harbors immense potential in mitigating climate change by substantially
reducing CO $ _ {2} $ emissions. Nonetheless, the inherent variability of solar irradiance …
reducing CO $ _ {2} $ emissions. Nonetheless, the inherent variability of solar irradiance …
Input convex Lipschitz RNN: A fast and robust approach for engineering tasks
Computational efficiency and non-adversarial robustness are critical factors in process
modeling and optimization for real-world engineering applications. Yet, conventional neural …
modeling and optimization for real-world engineering applications. Yet, conventional neural …