Submission Deadline: 15 December 2026 View: 125 Submit to Special Issue
Prof. Dr. Cotfas Daniel Tudor
Email: dtcotfas@unitbv.ro
Affiliation: Department of Electronics and Computers, Transilvania University of Brașov, Brașov, Romania
Homepage: https://www.unitbv.ro/en/contact/search-in-the-unitbv-community/6156-daniel-tudor-cotfas.html
Research Interests: renewable energy, photovoltaics, methods and metaheuristic algorithms for PV parameter extraction and forecasting the solar radiation and PV power, machine learning
Prof. Dr. Cotfas Petru Adrian
Email: pcotfas@unitbv.ro
Affiliation: Department of Electronics and Computers, Transilvania University of Brașov, Brașov, Romania
Homepage: https://www.unitbv.ro/contact/comunitatea-unitbv/6145-cotfas-petru-adrian.html
Research Interests: renewable energy, monitoring and characterization methods of the renewable energy sources, virtual instrumentation, hybrid systems
Assoc. Prof. Dr. Louzazni Mohamed
Email: louzazni.m@ucd.ac.ma
Affiliation: National School of Applied Sciences, Chouaïb Doukkali University, El Jadida, Morocco
Research Interests: mathematical modeling, optimization, metaheuristic algorithms, computational intelligence, optimization and control management of photovoltaic and power energy systems, forecasting, battery SOC and SOH forecasting, PVT modeling, fuel cell optimization
The intermittent nature of renewable energy requires new methods and algorithms to address the current energy needs necessary for sustainable development. Extracting parameters with high accuracy for photovoltaic panels, wind turbines, thermoelectric generators, and other devices used to produce electricity or heat from renewable sources is very important, both for research and manufacturers, but also for short, medium, and long-term forecasting of the energy that can be produced.
The use of advanced analytical and numerical methods, as well as existing or new AI algorithms, and their hybridization leads to considerable improvement both for parameter extraction and for the forecast of generated energy.
This special issue aims to collect original research and review articles in this domain to increase visibility of the innovative methods, algorithms, and experimental results. Potential topics include, but are not limited to the following:
· Metaheuristic algorithms for the extraction of the PV parameters
· Machine learning, deep learning, and others are used for PV diagnostics, reliability, and durability
· Methods and algorithms to forecast direct normal irradiance and its applications for the concentrated photovoltaic panel farms
· Solar potential estimation using empirical, parametric, and spectral models.
· AI algorithms for forecasting photovoltaic power for the short, medium, and long term
· AI algorithms for forecasting wind energy, thermal energy
· Metaheuristic algorithms for MPPT
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