Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting

Dear Colleague,

The main difficulty in solar energy production is the volatility intermittent of photovoltaic system power generation due mainly to weather conditions. Essentially, the variation of the temperature and irradiance can profoundly impact the quality of electric power production. As solar irradiance is highly related to solar power harvesting, its prediction can be a good indicator of power production. For large-scale solar farms, the power imbalance of the photovoltaic system may cause a significant loss in their economic profit. Thus, accurate solar irradiance prediction with appropriate modeling of PV systems is becoming vital to reduce the impact of uncertainty and energy costs and enable the suitable integration of photovoltaic systems in a smart grid. There have been many studies for models and algorithms to predict solar irradiance based on various meteorological factors that are routinely measured, such as temperature or humidity. Accurate forecast of solar irradiance and proper modeling of PV system behavior have become the backbone of smart grids due to the increasing installation of PV systems.

This Special Issue aims to collect original research or review articles in the areas of artificial intelligence applied to solar irradiance modeling/forecasting and PV system design. Thus, this call seeks submissions on innovative machine learning and deep learning methods for solar irradiance forecasting and PV systems modeling. 

Potential topics include but are not limited to:

• Solar irradiance modeling and forecasting
• Typical meteorological year (TMY) modeling
• PV system modeling
• Space–time prediction of solar irradiance
• Deep learning and machine learning methods
• Reinforcement learning

Dr. Fouzi Harrou
Dr. Ying Sun
Dr. Bilal Taghezouit
Dr. Abdelkader Dairi
Guest Editors

Manuscript Submission Information

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