Modelling, Optimisation and Forecasting of Photovoltaic and Photovoltaic thermal System Energy Production

Submission Deadline: 28 February 2025 View: 200 Submit to Special Issue

Guest Editors

Prof. Daniel Tudor Cotfas

Email: dtcotfas@unitbv.ro

Affiliation: Department of Electronics and Computers, Transilvania University of Brasov, Brasov, Romania

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Research Interests: renewable energy, energy harvesting, hybrid systems for photovoltaics, optoelectronics, virtual instrumentation, and remote engineering.

A.Prof. Mohamed Louzazni

Email: Louzazni.m@ucd.ac.ma

Affiliation: National School of Applied Sciences, Chouaïb Doukkali University of El Jadida, El Jadida, Morocco

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Research Interests: Mathematical Modelling, Optimisation and Meta-heuristic Algorithm, Computational Intelligence, Photovoltaic & Power energy, Forecasting, Fuel Cell, GPR, Radio frequency, Electromagnetic and electronic, Renewable Energy Technologies

Prof. Petru Adrian Cotfas

Email: pcotfas@unitbv.ro

Affiliation: Department of Electronics and Computers, Transilvania University of Brasov, Brasov, Romania

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Research Interests: characterization and monitoring of renewable energy systems; hybrid systems in the field of renewable energies IoT; virtual instrumentation

Summary

In recent years, the adoption of photovoltaic (PV) and Photovoltaic thermal (PVT) solar energy has surged due to its abundant, clean, and environmentally friendly characteristics. PV and PVT technology are modelled and simulated using specialized software applications, though challenges like high costs and limited availability of commercial packages persist. Graphical programming environments are effective for revealing PV and PVT system behaviours and analysing experimental outcomes. Mathematical modelling techniques allow accurate evaluation of PV and PVT system parameters and comparison of experimental results. Various methods exist for optimizing PV modules and PVT, including analytical, artificial intelligence, numerical, and hybrid approaches.

Accurate optimisation and forecasting are crucial for enhancing PV, PV-TE and PVT solar power plants, given the intermittent nature of solar energy generation due to weather fluctuations. Weather-induced variations can reduce PV and PVT energy output by over 20%, limiting integration into the power grid. Short-term forecasts of photovoltaic energy are vital for efficient electricity production and storage management. This Special Issue aims to showcase developments in modelling, optimisation and forecasting for PV and PVT energy production, enabling effective participation in the energy market and optimizing resource planning. Forecasting methods include statistical time series analysis, machine learning with artificial neural networks, physical modelling using weather data and satellite imagery, and hybrid approaches combining multiple techniques. Original research and review articles are welcomed on these topics.

· Optimization of PV and PVT system

· Optimization of PV-TE hybrid system

· Advanced modelling and simulation

· Evaluation of PVT module characteristics

· Impact of weather conditions on PV and PVT

· Real-time monitoring and control of PV and PVT systems

· Integration of PV and PVT systems into the power grid

· Forecasting techniques for PV and PVT energy management

· Application of advanced sensor technologies for

· Practical implementations showcasing and forecasting strategies

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