Solar AI Market Size, Share & Trends Analysis Report By Technology (Natural Language Processing, Machine Learning), By Application (Smart Grid Management, Demand Forecasting), By End Use, By Region, And Segment Forecasts

Published Date: May - 2025 | Publisher: MIR | No of Pages: 240 | Industry: technology | Format: Report available in PDF / Excel Format

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Solar AI Market Size & Trends

The size of the global solar AI market was estimated at USD 5.96 billion in 2024 and is anticipated to grow at a CAGR of 20.8% during the period from 2025 to 2035. Integration of AI into solar design platforms is picking up pace as businesses strive to automate and streamline the solar project lifecycle. Design platforms can, with AI, analyze satellite imagery, shading, roof geometry, and energy consumption patterns instantly and generate highly tailored system proposals. This integration reduces the time needed for manual surveys and engineering work, making project initiation much faster. It improves accuracy, helping installers minimize errors in system sizing, equipment selection, and installation planning.

Businesses implementing AI-powered design software are putting themselves in a position to manage more projects with less personnel. For example, in February 2025, Sunnova Energy International, Inc., a U.S.-based solar company, partnered with OpenSolar to add AI-based design to its Catalyst dealer platform, allowing it to create more accurate, quicker solar system proposals nationwide. Through the partnership, efficiency will increase, growing demand in the marketplace will be fulfilled, and the pace and accuracy of solar deployments will be elevated to a new standard.

AI models are revolutionizing the solar energy sector to a large extent by enhancing forecasting accuracy, which is vital for maximizing energy production and usage. By combining real-time weather patterns with past energy patterns, these models offer more accurate predictions regarding solar energy production. With this advanced forecasting, grid operators can efficiently manage the distribution and storage of energy so that supply keeps pace with demand. Since solar power is intermittent, having precise forecasts avoids imbalances and minimizes the chances of grid instability or blackouts. AI-based solutions also allow for better utilization of energy storage systems so that excess energy generated during peak sunlight hours is stored and utilized during low-generation hours.

The combination of solar power, battery storage, and artificial intelligence (AI) is gaining significance in the renewable energy sector, particularly for grid decarbonization. For example, Amazon.com, Inc.'s Baldy Mesa solar-plus-storage farm demonstrates how solutions powered by artificial intelligence are allowing for improved energy forecasting, storage optimization, and grid stability, which is vital since renewable energy generation is normally intermittent. The trend reflects a wider drive towards increasing the reliability of the energy system while diminishing reliance on fossil fuels in favor of cleaner sources. In addition, AI models assist in maximizing when and how power is to be stored or transmitted so as to have a constant supply of carbon-free energy, even outside peak hours of production. As investments in energy storage and artificial intelligence continue to rise, the solar industry is becoming a more efficient, responsive, and sustainable power market. This trend marks the transition to increasingly sophisticated grid integration technologies that will fuel the next generation of clean energy innovation.

Report Coverage & Deliverables

PDF report & online dashboard will help you understand

  • Competitive benchmarking
  • Historical data & forecasts
  • Company revenue shares
  • Regional opportunities
  • Latest trends & dynamics

Technology Insights

Technologically, the machine learning segment led the market and accounted for 49.9% in 2024 because it can improve prediction accuracy and system performance. It handles big data, such as weather conditions and energy production, to increase efficiency. Machine learning algorithms forecast energy production, detect possible failures, and optimize operations. These technologies learn from past experiences, providing useful insights for long-term planning and real-time decision-making. This ability is essential for optimizing the performance of solar energy systems.

The computer vision segment is projected to grow with the highest CAGR during the forecast period. It makes real-time inspection of solar panels and systems possible through the examination of visual information, permitting early fault detection. This technology helps in the detection of performance problems, thereby enabling faster intervention and reducing downtime. The capacity to automate checks and identify anomalies in solar installations means enhanced operational effectiveness. Further, computer vision optimizes the performance of solar assets so that they operate at maximum levels. It enables predictive maintenance, decreasing the rate of expensive repairs. Consequently, the use of computer vision in solar AI systems is emerging as a primary driver of the industry's growth.

Application Insights

By application, the Smart Grid Management segment accounted for the highest revenue in 2024. Grid management platforms that use AI in real-time monitor and manage energy flows. Such systems match supply and demand by varying solar input, storage, and distribution dynamically. Advanced analytics identify anomalies and avoid congestion, enhancing network reliability in general. Integration with demand-response programs allows utilities to better respond to grid fluctuations. Consequently, utilities are able to sustain grid stability as they incorporate higher solar generation levels. The technology plays a crucial role in maximizing the overall efficiency of grid operation and delivering a seamless supply of power.

The energy storage segment will grow at the highest CAGR during the forecasting period. Historical as well as real-time data are analyzed using AI algorithms to maximize battery charge and discharge schedules. This maximizes the utilization of stored solar energy during high-demand hours. Predictive models predict grid conditions and modify storage operations in response. These features minimize energy loss and improve grid resilience. Investors and operators are increasingly turning to AI-based storage solutions to enhance system performance and lower costs. With this technology, energy storage systems can run more efficiently, leading to reduced operational costs and increased returns on investment.  

End Use Insights

On the basis of end use, the industrial segment had the biggest revenue share in 2024. The segment dominates owing to the size and intensity of large solar installations. AI technologies like predictive maintenance and performance monitoring enable optimal operation and minimize unplanned downtime. Using machine learning, AI systems are able to inspect data from sensors to forecast equipment failures, which saves on maintenance costs. In addition, AI-based energy management systems improve grid stability through energy production and consumption forecasting. AI also helps the industrial sector in managing large amounts of data from various sources, including weather and system performance. With increasing industrial demand for solar power, AI adoption continues to simplify operations and enhance efficiency.

The commercial category is poised to expand at the highest CAGR during the forecasting period due to the growing preference for sophisticated solutions among companies. AI products enhance energy usage with optimization, oversee solar output, and synchronize with building management systems for increased command. Due to the heightened necessity for energy conservation, commercial solar installations are embracing AI for analyzing energy movement, minimizing loss, and curbing operational costs. Predictive analytics in AI enable companies to better predict energy production and demand, reducing costs and increasing profitability. AI also optimizes energy storage management, maximizing storage and distribution of solar energy. With increasing commercial properties turning to renewable energy, AI solutions are becoming the key to meeting sustainability targets and lowering carbon footprints.

Market Size

Regional Insights

North America solar AI market dominated the global industry and accounted for a 36.8% share in 2024. The solar AI market in North America is experiencing rapid growth, driven by an increasing focus on clean energy solutions and grid modernization. The integration of AI with solar and battery storage technologies is helping optimize energy management and ensure a stable supply of power. With strong government incentives and a growing renewable energy infrastructure, the market is expected to expand significantly.

U.S. Solar AI Market Trends

The solar AI industry in the U.S. is particularly active, with companies utilizing AI to enhance energy storage systems and optimize grid operations. The country’s commitment to reducing carbon emissions and transitioning to renewable energy sources has led to the widespread adoption of AI-driven solutions in solar projects. As the largest market in North America, the U.S. is a key player in advancing AI technologies that improve solar efficiency and contribute to decarbonizing the grid.

Europe Solar AI Market Trends

The solar AI industry in Europe is at the forefront of integrating AI with solar energy, with countries such as Germany and Spain making significant strides in solar AI adoption. AI technologies are being used to enhance grid stability, optimize solar power generation, and reduce reliance on fossil fuels. The European Union's green energy policies and ambitious climate targets are accelerating the deployment of solar AI solutions. As the region continues to prioritize renewable energy, the market for solar AI technologies is set for substantial growth.

Asia Pacific Solar AI Market Trends

The solar AI industry in Asia Pacific is growing as countries such as China, India, and Japan are increasingly adopting AI in the solar energy sector to tackle energy demand challenges and improve grid reliability. AI-driven technologies are being deployed to optimize solar power generation, energy storage, and forecasting, particularly in areas with high solar potential. The region's rapid industrialization and growing emphasis on sustainability are fueling the demand for smarter, more efficient solar solutions.

Key Solar AI Company Insights

Key players operating in the solar AI market are undertaking various initiatives to strengthen their presence and increase the reach of their products and services. Strategies such as expansion activities and partnerships are key in propelling the market growth.

Market Analysis

Key Solar AI Companies

The following are the leading companies in the solar AI market. These companies collectively hold the largest market share and dictate industry trends.

Recent Developments

  • In March 2025, Goldi Solar, Inc. inaugurated India's first AI-based solar manufacturing plant in Surat, which will improve the speed, efficiency, and accuracy of solar panel manufacturing. The plant boasts sophisticated AI technologies like high-speed stringers, automated optical inspection, and AI-based visual inspection, which will decrease errors, reduce wastage of materials, and enhance production quality, aiding India's clean energy future.

    In September 2024, SmartHelio, a Swiss solar software company, unveiled an AI-driven suite, de-risking solar investments. The Suite incorporates predictive analytics, a Climate Risk Assessment (CRA) tool, and an Autopilot solution, allowing solar plant operators to maximize resource allocation, minimize costs, and enhance the overall system performance.

    In July 2024, AES Corporation launched Maximo, a revolutionary AI-based robot that can transform solar panel installation. The robot is capable of doing installations in half the time and half the cost and collaborates with construction teams to improve speed, safety, and scalability. Maximo's AI capabilities, such as computer vision and ongoing learning, provide accurate panel placement and enhance overall installation efficiency.

Solar AI Market Report Scope

Report Attribute

Details

Market size value in 2025

USD 7.15 billion

Revenue forecast in 2030

USD 18.43 billion

Growth rate

CAGR of 20.8 from 2025 to 2035

Base year for estimation

2024

Historical data

2018 - 2023

Forecast period

2025 - 2035

Quantitative units

Revenue in USD million/billion and CAGR from 2025 to 2030

Report coverage

Revenue forecast, company ranking, competitive landscape, growth factors, and trends

Segments covered

Technology, application, end use, region

Regional scope

North America; Europe; Asia Pacific; Latin America; Middle East & Africa

Country scope

U.S.; Canada; Mexico; Germany; UK; France; China; Japan; India; Australia; South Korea; Brazil; KSA; UAE; South Africa

Key companies profiled

AES Corporation; ENFOR; Goldi Solar, Inc.; Heliogen, Inc.; Scopito; Solar Infra Pvt. Ltd.; Solavio; Smart Helio; Suncast; Tespack

Customization scope

Free report customization (equivalent up to 8 analysts’ working days) with purchase. Addition or alteration to country, regional & segment scope

Pricing and purchase options

Avail customized purchase options to meet your exact research needs. Explore purchase options

Global Solar AI Market Report Segmentation

Technology Outlook (Revenue, USD Million, 2018 - 2035)

    • Natural Language Processing

    • Machine Learning

    • Computer Vision

    • Others

  • Application Outlook (Revenue, USD Million, 2018 - 2035)

    • Smart Grid Management

    • Demand Forecasting

    • Energy Production

    • Smart Meters

    • Energy Storage Optimization

    • Predictive Maintenance

    • Solar Panel Design

    • Weather Forecasting

    • Solar Farm Operations

    • Others

  • End Use Outlook (Revenue, USD Million, 2018 - 2035)

    • Industrial

    • Commercial

    • Residential

  • Regional Outlook (Revenue, USD Million, 2018 - 2035)

    • North America

      • U.S.

      • Canada

      • Mexico

    • Europe

      • U.K.

      • Germany

      • France

    • Asia Pacific

      • China

      • Japan

      • India

      • Australia

      • South Korea

    • Latin America

      • Brazil

    • Middle East & Africa (MEA)

      • KSA

      • UAE

      • South Africa

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