Evolution of Solar

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].

Solar has the highest growth rates of any renewable energy source in recent years (Figure 1). This growth has been accompanied by major and rapid 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. 

Figure 1.  Total renewable capacity additions by technology, 2019-2024, by IEA

In this context, the integration of Artificial Intelligence (AI) into the field of PV is playing a crucial role in revolutionizing PV plant operations by enabling predictive maintenance, real-time monitoring and enhanced energy management. AI has the potential to optimize various aspects of PV production. 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. In contrast, the primary challenges could be the availability and quality of data, the technical integration, and the skill level of the workforce.

Opportunities in AI for Photovoltaics

One of the significant benefits of using AI in photovoltaics is its potential to improve the efficiency of solar panels. AI algorithms can process vast amounts of data from sensors embedded in solar panels to track their performance in real time. This is one of PVOP’s main goals.

These systems can monitor temperature, humidity and light intensity, allowing for dynamic adjustments to optimize the energy production of solar panels. 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. AI-powered predictive maintenance is another key opportunity.

Traditional maintenance often relies on scheduled inspections or reactive measures. By integrating AI, PV systems can predict equipment failures before they happen, reducing the downtime and costs associated with these failures. AI models can identify patterns that signal an impending malfunction, allowing technicians to perform targeted repairs before they cause system-wide disruptions. This approach extends the lifespan of solar equipment and reduces Operation and Maintenance (O&M) costs.

Additionally, AI can streamline the design and installation processes of PV plants, identifying the best locations for installations, considering sunlight exposure, local weather conditions and land availability.

AI can also automate the design process by calculating the optimal size and configuration of the solar array to maximize energy output and 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 optimize when and how to release stored energy to the grid.

This predictive capability ensures more efficient energy use and can help stabilize the grid by balancing solar energy supply with consumption demand. The integration of solar power into the broader electrical grid is a complex challenge, because solar energy production is variable and decentralized.

AI helps develop smart grids that manage the dynamic and fluctuating nature of photovoltaics, ensuring efficient distribution and minimizing waste. AI also assists with integrating diverse energy sources into the grid, improving energy security and making renewable energy more reliable.

Challenges in AI for Photovoltaics

On the other hand, challenges such as data quality, high computational requirements, integration with existing systems, and skill workforce gaps must be addressed to ensure successful AI adoption in photovoltaics. Finally, regulations surrounding data privacy, ownership, and consent will need to be clearly defined to ensure privacy rights.

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. Additionally, the collection of large-scale datasets necessitates substantial infrastructure investments and long-term monitoring commitments, which may not always be feasible. Machine learning and AI algorithms, particularly deep learning models, often require significant computational resources.

Training these models requires high-performance computing (HPC) systems, which can be costly and energy-consuming. For widespread adoption, PV companies must balance the benefits of AI with the environmental footprint of the infrastructure required to power these AI systems. Moreover, the computational power necessary for AI deployment can pose scalability challenges, particularly in small or medium-sized solar operations with limited budgets.

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. 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 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: Rodrigo Moretón and Oscar Anchorena (QPV)