# Applications of Artificial Intelligence to Photovoltaic Systems: A Review  
[PERSON]  
1 Departamento Fisica de la Materia Condensada, Universidad de Valladolid, 47011 Valladolid, Spain 1  
[PERSON]  
1 Departamento Fisica de la Materia Condensada, Universidad de Valladolid, 47011 Valladolid, Spain 1  
[PERSON]  
2 Departamento Informatica, Universidad de Valladolid, 47001 Valladolid, Spain 2  
[PERSON]  
3 Departamento de Fisica, Universidad de Valladolid, 47002 Valladolid, Spain 3  
[PERSON]  
4 Departamento Ingenieria Agricola y Forestal, Universidad de Valladolid, 47002 Valladolid, Spain 4  
[PERSON]  
5 Department of Mechanical Engineering, Durham University of Technology, P.O. Box 1334,  
Durban 4000, South Africa 5  
and [PERSON]  
4 Corresponded:  
PV energy is produced by photovoltaic modules. Each module is composed of different sub-units, called solar cells, which absorb the energy emitted by the sun [8]. PV panels are usually connected in series to each other, this is known as a PV array. Each PV array is connected to a power inverter to control the production and check the performance of the array [9]. PV farms (also known as PV plants) are usually composed of many PV arrays. The maintenance of these factories is extremely complex. The production of the modules depends on different conditions, this makes mechanisms of control to optimize the production necessary. Solar modules are also vulnerable to physical defects, which can reduce or even nullify the production of one cell, or even the whole modules in the worst cases. This is usually dealt with by human labor, checking each module in a certain period of time. Solutions to maintenance problems in PV systems have been traditionally circumscribed either to simplistic automatic supervision approaches [10] or costly direct human supervision. In the past recent years, however, artificial intelligence (AI)-based approaches have emerged. AI techniques are being applied in almost every research field or industry to improve services or solve problems which are impossible for traditional methods [11]. These techniques can also be applied to solve the problems of PV systems. In this review, we analyze how AI is being applied to the PV sector. PV systems face different problems during installation but also during exploitation, since PV modules are vulnerable to the climate conditions' unpredictable events. An analysis of the problems found in PV plants can be found in [12, 13]; also, the maintenance of the modules is key in order to secure maximum production and to improve the security of the installations [14]. In order to reduce the scope of this review, only the four most critical problems related to energy optimization and maintenance are considered:  
* **Max Power Point Tracking:** Vital for optimizing the production. * **Output Power Forecasting:** Critical for predicting possible problems in production related to climate conditions. * **Parameter Estimation:** Extremely important for optimizing the production of PV modules. * **Defect Detection:** Important for finding bad-performing modules or faults that can be fatal for overall system performance and security. The most important problems and the technologies that have been used for dealing with each one of these problems are addressed. The final objective of this review is to analyze the most important techniques used and how they have improved the solutions of the problems in order to have a clear understanding of the state of the art in the area. The paper is structured as follows: First, an explanation of the problem is described in Section 2, in order to provide more information to readers who are not familiarized with this topic. After that, the artificial intelligence techniques used of each of the problems are explained. The Maximum Power Point Tracking is discussed in Section 3.2; Section 3.3 is about the forecasting; Section 3.4 presents the parameter estimation, and defect detection problems are discussed in Section 3.5.