According to a new report by UnivDatos, the Solar AI market is expected to reach USD XX million in 2033 by growing at a CAGR of 16.8% during the forecast period (2025-2033).

With AI-enabled predictive maintenance emerging as a potential engine of growth, the international Solar AI market is undergoing rapid changes. With the growing number of solar installations, the demand for Solar AI solutions is emerging rapidly. The assets get degraded with time owing to environmental stressors and component fatigue. AI-enabled predictive maintenance tries to put a check on this by continuously monitoring system health and predicting failure mode just before the actual occurrence. During the whole process, advanced analytics and ML models work on data obtained from solar panels, inverters, and sensors so that the maintenance team can take corrective action even before the occurrence of any actual failure. This reduces their repair costs, maximizing uptime and energy yield.

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With the rising demand for solar power across the globe, the following are some of the key updates for the Solar AI market:

·       According to the US Information Administration, solar power generation will increase by 26 GW and 22 GW in the years 2025 and 2026, respectively. It will be a massive opportunity for the companies that are looking forward to expanding their AI solutions in the solar power plants across the US.

·       In 2025, Saudi Arabia announced the construction of 7 new solar power plants under its Saudi Vision 2030. According to the government, the total installed solar power capacity is 2.1 GW PV, and 5.3 GW PV is under construction.

Segments that transform the industry

·       Based on technology, the Solar AI market is segmented into natural language processing, machine learning, computer vision, and others. Machine learning has been considered the largest-growth application in the Solar AI market, its proliferating development backed by the capability to process huge operational data from solar installations to derive useful insights. ML algorithms are used in predictive maintenance, forecasting energy output, detecting faults, and optimizing performance. Increasingly, with the sensors and IoT devices being deployed on solar farms, the ML models can continue to learn and evolve in accuracy with time; that kind of dynamic adaptation places machine learning ahead of rule-based systems. Moreover, ML is being embedded into energy management platforms and digital twin models whose purpose is to simulate the system behavior under multiple conditions for better planning and asset utilization. As solar energy is scaled globally, intelligent and automated solutions will thus create demand, keeping machine learning technology on top in the Solar AI market.

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According to the report, a Rising number of Solar Installations have been identified as key drivers for market growth. Some of how this impact has been felt include:

The rising focus on energy generation from renewable sources has led to higher adoption of solar power plants. Countries receiving an ample amount of sunlight throughout the year are extensively focusing on the installation of solar power projects in order to reduce dependence on fossil fuel sources of energy.

·       According to the International Energy Agency (IEA), the total share of solar power in global electricity production was 6.0% in the year 2020, which rose to 8.2% in the year 2024. Additionally, as per the estimates, the global mix of solar energy is anticipated to reach 16.1% by the year 2030, as per the IEA.

·       Additionally, many of the large-scale projects have recently been completed across different countries, which would be conducive for the adoption of Solar AI technologies. For instance, in 2024, the construction of the Hobq Solar Park, China was completed. The plant has a total capacity of 4 GW and has a planned capacity expansion of up to 13.5 GW.

Recently, many companies have also announced their plans to establish new solar power plants, which would be crucial for the future adoption of AI-based technologies. Considering these factors, the market for Solar AI is anticipated to rise during the forecasted years, i.e., 2025-2033.

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