The solar energy market has seen significant growth in recent years, with annual installation growth rates remaining at very high levels. For instance, the International Energy Agency (IEA) has reported a 30% year-on-year increase in photovoltaics (PV) solar installation, totaling approximately 550 GW, in 2024[1]. This growth has been accompanied by major and rapid technological innovation. Such a technological revolution is set to have far-reaching consequences, the full extent of which is yet to be determined. Additionally, this transformation brings with it significant risks and challenges, including the need for enhanced management capacity, strategic foresight, and effective maintenance of the expanding array of sophisticated assets. We are faced with a huge amount of information from thousands of distributed elements that need to be reviewed and optimized.
In this context, the integration of Artificial Intelligence (AI) into the field of PV plays a crucial role in revolutionizing PV plant operations. AI is able to learn patterns of behavior and analyze large amounts of data based on them almost instantaneously. This capability will enable us to reach new levels of plant supervision and analysis, from the point of view of enhanced performance, predictive maintenance, or security and robustness. However, as with any emerging technology, there are several challenges that need to be addressed to fully deliver the potential of AI in the photovoltaics industry. This article explores both the opportunities and challenges of using AI in photovoltaics. Among the former can be mentioned improved panel efficiency and enhanced predictive maintenance, a better installation design, optimization of energy storage and grid integration.
Opportunities in AI for Photovoltaics
Today’s solar plants are reaching large sizes, with hundreds or thousands of generating elements and sensors across their entire surface. Solar power is moving from the GW era to the TW era. This information is used both in real time and historically to operate and analyse the installation. In all plants, the personnel in charge are assisted by classic tools of industrial supervision and control. However, these tools often have limitations when it comes to optimising operations or diagnosing anomalies. In such cases, operators and analysts require a new tool to assist them in carrying out this task, namely a tool capable of identifying patterns of anomalous or optimisable behaviours within a huge amount of data. AI is emerging as a key tool to provide this support.
To understand the great capacity that AI can have in the analysis of solar plants, it is necessary to highlight one of its main qualities: its modularity. A solar power plant typically consists of a series of highly similar generation blocks, both in terms of electrical output and data. This means that identifying an anomalous pattern in one part of the plant can facilitate the identification of similar patterns in other parts, thereby accelerating the learning process. This is especially valuable in the use of machine learning, where manual evaluation of an anomaly is applied to the supervised learning process. It also facilitates the application of unsupervised classification techniques, which are based on inter-element comparison. In addition, although each plant has its particularities, the error patterns are similar in all of them, which allows the effective application of transfer learning.
This learning can grow rapidly, applying to any plant production problem and identifying it in situations where classical techniques cannot. Machine learning models can even predict the effects of shading, dirt accumulation, or panel degradation, enabling maintenance teams to take proactive measures to ensure optimal performance. By integrating AI, PV systems can predict equipment failures before they happen, reducing the downtime and costs associated with these failures. This approach is intended to extend the lifespan of solar equipment and ensure uninterrupted energy production. AI can also facilitate the diagnosis of complex patterns and variants, enabling technicians to perform specific repairs more efficiently, reducing work time and improving their safety.
Artificial Intelligence plays a crucial role in revolutionizing photovoltaics plant operations. Photo by Nuno Marques on Unsplash.
Additionally, AI can streamline the design and installation processes of PV plants, analyzing geographical and environmental data to identify the best locations for installations, considering sunlight exposure, local weather conditions and land availability. AI has the potential to automate the design process by calculating the optimal size and configuration of the solar array to maximize energy output. It can also improve the installation process by guiding robots or drones to conduct installations with precision, reducing the likelihood of human error and lowering labor costs.
In recent years, there has been significant progress in the field of battery technology, with improvements in efficiency and installation rates. AI can also play a crucial role in optimizing energy storage systems, as algorithms can help predict demand patterns and determine the most efficient times to release stored energy to the grid. This predictive capability is key to ensuring more efficient energy use and can help stabilize the grid by balancing solar energy supply with consumption demand.
Integrating solar power into the broader electrical grid poses a complex challenge. Solar energy production is often variable and decentralized, and has become one of the main generation technologies, having to adapt to the grid and the control of the electricity system. AI can help in developing smart grids that are capable of managing the dynamic and fluctuating nature of renewable energy sources like solar. These smart grids use AI to predict fluctuations in energy production and consumption, ensuring efficient distribution and minimizing waste. AI can also assist with integrating diverse energy sources into the grid, improving energy security and making renewable energy more reliable. In addition, as it can be controlled from the outside, it is also vulnerable to attacks. Access to control of these installations, especially in a coordinated manner, could lead to grid instabilities or even blackouts. Regardless of specific information security measures, AI can help identify malicious control patterns and warn if the facility is under attack.
Challenges in AI for Photovoltaics
On the other hand, challenges such as data quality and privacy, computational requirements, integration with existing systems, and skill workforce gaps must be addressed to ensure successful AI adoption in photovoltaics.
The potential of AI to enhance photovoltaic technology is contingent on the availability of high-quality data. The quality of data gathered from solar panels, sensors and environmental conditions may introduce noise, which can make it difficult to train effective AI models. Weather variability introduces one of the main challenges at the technical level. This causes failure patterns to be dependent on different situations and their repeatability is not entirely clear. For this, algorithms must work with multiple examples with different operating conditions, requiring large numbers of verified cases to reach high levels of accuracy. If we add to this the fact that a full year is required to be able to be before all possible conditions, the less recurrent anomalies can be difficult to characterize. Whenever working with AI, the increase in computational needs must not be forgotten. However, although the number of elements is large, it does not have the same processing requirements at the time of operation as other applications, such as real-time image processing. Therefore, although the computational needs increase, it does not appear to be a real challenge for this application.
Integrating AI into existing PV systems presents another challenge. Many installations were designed and deployed before AI technologies became widespread. Retrofitting these older systems with AI-enabled sensors, controllers, and communication networks can be complex and costly. Ensuring that AI models work seamlessly with these existing systems requires a robust understanding of both the technical specifications of older systems and the new AI technologies being implemented. Compatibility issues may arise, which could hinder the full potential of AI in older installations.
Lastly, the deployment of AI in photovoltaics requires specialized knowledge in both PV systems and AI technologies. However, there is a shortage of professionals with the necessary skills to bridge these two fields. As the solar energy industry increasingly adopts AI, there will be a growing demand for workers with expertise in data science, machine learning, and energy systems. To fully realize the potential of AI, the workforce will need to undergo training and development, which will take time and investment.
Looking to connect with other professionals in the PV sector? Join the PVOP community and start networking with other professionals.
[1] Global Energy review 2025 on electricity Electricity – Global Energy Review 2025 – Analysis – IEA
Article by: Manuel Castillo, Rodrigo Moretón and Oscar Anchorena from Qualifying Photovoltaics (QPV).