This article analyzes the relationship between artificial intelligence (AI) and photovoltaic (PV) systems. Solar energy is one of the most important renewable energies, and the investment of businesses and governments is increasing every year. AI is used to solve the most important problems found in PV systems, such as the tracking of the Max Power Point of the PV modules, the forecasting of the energy produced by the PV system, the estimation of the parameters of the equivalent model of PV modules or the detection of faults found in PV modules or cells. AI techniques perform better than classical approaches, even though they have some limitations such as the amount of data and the high computation times needed for performing the training. Research is still being conducted in order to solve these problems and find techniques with better performance. This article analyzes the most relevant scientific works that use artificial intelligence to deal with the key PV problems by searching terms related with artificial intelligence and photovoltaic systems in the most important academic research databases. The number of publications shows that this field is of great interest to researchers. The findings also show that these kinds of algorithms really have helped to solve these issues or to improve the previous solutions in terms of efficiency or accuracy.

Affordable and clean energy is one of the Sustainable Development Goals (SDG). SDG compliance and economic crises have boosted investment in solar energy as an important source of renewable generation. Nevertheless, the complex maintenance of solar plants is behind the increasing trend to use advanced artificial intelligence techniques, which critically depend on big amounts of data. In this work, a model based on Deep Convolutional Generative Adversarial Neural Networks (DCGANs) was trained in order to generate a synthetic dataset made of 10,000 electroluminescence images of photovoltaic cells, which extends a smaller dataset of experimentally acquired images. The energy output of the virtual cells associated with the synthetic dataset is predicted using a Random Forest regression model trained from real IV curves measured on real cells during the image acquisition process. The assessment of the resulting synthetic dataset gives an Inception Score of 2.3 and a Fréchet Inception Distance of 15.8 to the real original images, which ensures the excellent quality of the generated images. The final dataset can thus be later used to improve machine learning algorithms or to analyze patterns of solar cell defects.

An adaptive neuro-fuzzy inference scheme for defect detection and classification of solar PV cells

Optimal design of laminated composite and nanocomposite structures using evolutionary optimization techniques: a survey

This study offers a detailed sensitivity analysis of the elastic behavior of graphene/fiber‐reinforced nanocomposites using a multiscale micromechanical modeling framework. It focuses on key elastic properties, which are the longitudinal modulus (), transverse modulus (), in‐plane shear modulus (), and Poisson's ratio (), and examines how changes in graphene nanoplatelet (GPL) content affect the mechanical response of nanocomposites reinforced with carbon or glass fibers. The study derives closed‐form expressions for the first and second derivatives of each elastic property with respect to GPL volume fraction to measure the rate of change and identify critical saturation points where additional reinforcement provides diminishing benefits. Its main contribution is the comparative approach and emphasis on rate‐based analysis rather than just magnitude‐based improvements. By identifying sensitivity patterns and reinforcement efficiency thresholds for each elastic constant, the study creates material‐specific design guidelines to optimize GPL use in multiscale composite systems. The results lay a foundation for the rational design of lightweight, high‐stiffness nanocomposites suited for advanced structural applications.