Photovoltaic (PV) solar energy, based on PV systems or power plants, has increased in recent years due to its advantages of being abundant, inexhaustible, clean, and environmentally friendly. Modeling and simulation of PV technology and systems can be accomplished using different software applications, as there are many software packages in the area of modeling, simulation, and analysis of PV systems. However, they have some disadvantages, such as expensive software, only commercially available packages, and interfacing problems with electronic power systems, among others.

There are several PV modeling techniques. One of the best approaches is to consider PV models based on certain graphical programming environments, as these can reveal the operational and behavioral characteristics of PV systems and can also analyze experimental results with simulation outcomes. These packages are also user-friendly, flexible, develop accurate models, simulate complex models, and provide many applications in PV systems. Another approach is to describe mathematically PV models in order to evaluate different model parameters accurately and compare experimental and simulated outcomes. There are various methods of modeling and optimization of PV modules, such as analytical methods, linearization methods, artificial intelligence methods, numerical methods, artificial neural networks, and fuzzy methods and genetic algorithms. Reliable and accurate forecasts play a key role in improving PV solar power plants. The main challenge in the production of solar energy is the intermittent generation of electricity using PV systems due to weather conditions. Variations in temperature and solar irradiance can have a deep impact on the quality of electricity production, leading to a decrease of more than 20% in PV energy production provided by real PV installations. This limits the integration of PV systems into the grid. Therefore, an accurate short-term forecast of photovoltaic energy is very useful for the efficient management of electricity production and storage in the grid.

The aim of this Special Issue is to collect the latest developments in modeling and forecasting for energy production in PV systems. Accurate forecasting of solar energy is essential for PV power plants, in order to facilitate their participation in the energy market and for efficient resource planning. The forecasting methods/models can be divided into four classes, and we welcome studies into each: statistical approaches to time series forecasting, using measured historical data; machine learning techniques, in particular artificial neural networks; physical models based on numerical weather prediction and satellite imagery; and hybrid approaches. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Modeling of PV systems based on experimental data
• Reliability modeling of PV systems
• Numerical modeling of PV systems
• Modeling of PV modules and identification of physical and technical parameters
• Design of maximum power point tracking (MPPT) modeling of PV systems
• Analytical approaches for modeling PV systems
• Modeling, design, and control of distributed maximum power point tracking (DMPPT)
• Forecasting energy production based on statistical approaches and measured historical data (ARIMA)
• Forecasting energy production based on machine learning techniques, in particular artificial neural networks (ANN)
• Forecasting energy production based on numerical weather prediction and satellite imagery
• Forecasting energy production based on hybrid models
• Modeling of photovoltaic energy in the built environment
• Forecasting of energy produced in buildings with building integrated photovoltaics (BIPV) or building applied photovoltaics (BAPV)
• Photovoltaic energy monitoring and forecasting in communities
• Numerical modeling of BIPV systems
• Advanced forecasting of PV power plants/PV parks
• Numerical modeling of applications based on PV systems
• Modeling and control of grid-connected PV systems for smart grid integration
• Integration of PVs in small isolated systems
• Grid interaction, storage, and management of PV systems