What are the recent trends in market size and growth for the solar artificial intelligence market?
The solar artificial intelligence market size has grown rapidly in recent years. It will grow from $0.92 $ billion in 2024 to $1.06 $ billion in 2025 at a compound annual growth rate (CAGR) of 15.3%. The growth in the historic period can be attributed to growing demand to cut electricity prices and energy waste, increase investments in the solar industry, increase carbon reduction, and increase energy demand.
The solar artificial intelligence market size is expected to see rapid growth in the next few years. It will grow to $1.84 $ billion in 2029 at a compound annual growth rate (CAGR) of 14.9%. The growth in the forecast period can be attributed to the increasing deployment of solar energy systems, the increasing focus on reducing carbon emissions, the growing utilization of artificial intelligence, the growing focus on renewable energy sources, the rising need for sustainable energy solutions, and the rising demand for solar power systems in agricultural activities. Major trends in the forecast period include the integration of artificial intelligence-driven solutions, the integration of solar energy into existing power grids, advanced artificial intelligence technologies, innovative energy generation models, and technological advancements.
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How have varous drivers impacted the growth of the solar artificial intelligence market?
The growing focus on renewable energy is expected to propel the growth of the solar artificial intelligence market going forward. Renewable energy refers to energy sourced from natural processes that are continually replenished, such as sunlight, wind, and water. The increasing emphasis on renewable energy is fueled by the urgent need to address climate change and cut greenhouse gas emissions, coupled with technological advancements and falling costs that enhance the viability and appeal of these technologies. Solar artificial intelligence boosts the focus on renewable energy by optimizing energy production with predictive maintenance and efficient grid management, while also enhancing solar forecasting accuracy for improved integration into the energy grid. For instance, in June 2023, according to the U.S. Energy Information Administration, a US-based government agency, renewable energy made up about 13% of total U.S. energy consumption in 2022, with the electric power sector using 61% of this and renewables contributing 21% to U.S. electricity generation. Therefore, the growing focus on renewable energy drives the growth of solar artificial intelligence market.
What are the primary segments of the solar artificial intelligence market?
The solar artificial intelligencemarket covered in this report is segmented –
1) By Technology: Natural Language Processing, Machine Learning, Computer vision, Other Technologies
2) By Application: Energy Management, Smart Grids, Energy Production, Smart Meters, Demand Forecasting, Other Applications
3) By End-Use: Residential, Commercial, Industrial
Subsegments:
1) Natural Language Processing: Text Analysis, Voice Recognition, Chatbots
2) Machine Learning: Predictive Analytics, Pattern Recognition, Data Mining
3) Computer Vision: Image Recognition, Object Detection, Video Analysis
4) Other Technologies: Robotics, Expert Systems, Neural Networks
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Which firms are leading the solar artificial intelligence market?
Major companies operating in the solar artificial intelligence market are Nvidia Corporation, Trina Solar Limited, The AES Corporation, JinkoSolar Holding Co. Ltd., Enphase Energy Inc, SunPower Corporation, Heliogen Inc, SolarWinds AI, Waaree Energies Ltd, Suncast Corporation, Aurora Solar Inc, BrightSource Energy Inc, Tespack Ltd, TransitionZero, GreenMatch AG, Glint Solar AS, Scopito ApS, Absolar Solutions Ltd, Solarify GmbH, GetSolar LLC, Loggma Technologies Pvt Ltd, Omdena Inc., Raycatch Ltd., Smart Helio SA, Aigen Technologies Inc.
How will industry trends affect the trajectory of the solar artificial intelligence market?
Major companies operating in the solar artificial intelligence market are focusing on expanding their service offerings by developing technologically advanced solutions, such as AI-enabled solar installation robots, to enhance efficiency and reduce operational costs. AI-enabled solar installation robots are advanced systems that use artificial intelligence to automate and optimize the placement and installation of solar panels, enhancing efficiency and accuracy while reducing labor costs. For instance, in July 2024, AES Corporation, a US-based energy company, launched Maximo, the first AI-enabled solar installation robot that can install panels in half the time and at half the cost compared to traditional methods. This robot includes AI-powered computer vision for precise panel placement, continuous learning for efficiency improvements, and image reconstruction to enhance operational effectiveness in various environments. With Maximo, AES aims to install 100 MW of solar capacity by 2025 and build up to 5 GW of its solar backlog over the next three years, including significant projects like the 2 GW Bellefield solar-plus-storage project in California. The introduction of Maximo is expected to create new high-tech job opportunities and engage a broader workforce, while also accelerating project timelines and enhancing the scalability of solar installations as the demand for renewable energy grows, driven by factors such as the rise of AI and data centers.
Which geographic trends are shaping the solar artificial intelligence market, and which region has the highest market share?
Asia-Pacific was the largest region in the solar artificial intelligence market in 2024.
What Does The Solar Artificial Intelligence Market Report 2025 Offer?
The solar artificial intelligence market research report from The Business Research Company offers global market size, growth rate, regional shares, competitor analysis, detailed segments, trends, and opportunities.
Solar artificial intelligence refers to the use of AI technologies to optimize solar energy systems by enhancing efficiency, predicting maintenance needs, and improving grid integration. This integration helps maximize energy production and streamline the management of solar power.
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