The global artificial intelligence in renewable energy market size was valued at $0.6 billion in 2022, and is projected to reach $4.6 billion by 2032, growing at a CAGR of 23.2% from 2023 to 2032.
Artificial intelligence in renewable energy involves the application of AI techniques, such as computer mastering and facts analytics, to improve the generation, storage, distribution, and consumption of renewable electricity sources. It encompasses a wide range of technologies, including predictive modeling, optimization algorithms, and independent manage systems, to maximize the utilization of renewable resources like solar, wind, and hydropower.
AI-driven predictive modeling can accurately forecast solar irradiance and climate patterns, enabling better energy technology forecasts. AI helps optimize the positioning of photo voltaic panels and trackers to seize most sunlight at some stage in the day, for that reason improving the average efficiency of solar strength systems.
Wind farms gain notably from AI applications. Machine studying algorithms analyze sizeable amounts of records to predict wind patterns and turbine performance. This approves for higher turbine protection scheduling and greater unique energy generation. AI helps in the management of electricity distribution grids. Machine mastering algorithms analyze real-time facts to predict power demand and pick out practicable faults or disturbances in the grid. Artificial brain permits grid operators to respond proactively, making sure a stable and reliable furnish of renewable power to consumers.
AI has been employed to protect birds and bats. Advanced pc vision structures can realize the presence of birds close to mills and briefly shut them down to forestall collisions. This modern method addresses ecological issues related with wind farms. AI is instrumental in enhancing the resilience of strength grids. Artificial intelligence structures can additionally predict and mitigate doable cyberattacks, making sure the integrity and security of renewable power infrastructure.
Artificial intelligence is being used to improve desalination approaches powered by means of renewable energy. AI algorithms optimize the operation of desalination plants, decreasing electricity consumption and charges whilst increasing freshwater production. AI is employed in the plan and optimization of floating photo voltaic farms on our bodies of water. These farms are rising as a sustainable solution for land-constrained regions, and AI helps decide the most efficient layout and protection strategies.
The artificial intelligence in renewable energy market growth is segmented into deployment type, component type, end-use industry, and region. On the basis of deployment type, the market is bifurcated into on-premises and cloud. On the basis of component type, the market is divided into solution, and service. On the basis of end-use industry, the market is classified into energy generation, energy transmission, energy distribution, and utilities. On the basis of region, the market is studied across North America, Europe, Asia-Pacific, and LAMEA.
By deployment type, on-premises is the fastest-growing segment in the artificial intelligence in renewable energy industry in 2022. AI systems can analyze real-time records from on-premises renewable energy sources like solar panels and wind turbines. They can optimize energy production by means of adjusting the perspective of photo voltaic panels, the orientation of wind turbines, and battery storage usage to maximize power technology and consumption. AI models can furnish correct strength production forecasts primarily based on climate conditions, historical data, and different factors. This helps on-premises customers design their power consumption and storage greater effectively.
Based on component type, service is the fastest-growing segment in the AI in renewable energy market in 2022. AI-based grid administration systems can stability the intermittent nature of renewable strength sources. These structures use real-time statistics to control energy distribution efficiently, ensuring a secure supply. AI can optimize electricity storage systems, such as batteries. It can determine when to store excess energy and when to release it, making the most of renewable energy production.
Based on end-use industry, energy distribution is the fastest-growing segment in the AI in renewable energy market in 2022. AI can help manage energy storage systems, such as batteries. Machine gaining knowledge of algorithms can optimize the charging and discharging of batteries primarily based on grid demand and renewable strength availability, maximizing the use of clean power and reducing reliance on fossil fuels. AI can play a crucial role in improving the resilience of strength grids in opposition to disruptions, including those brought about by using extreme climate events. Predictive maintenance algorithms can discover attainable issues in the grid infrastructure earlier than they lead to outages.
Region-wise Asia-Pacific is the fastest-growing and dominated in the market in 2022. In the Asia-Pacific region, hydropower performs a good sized role as a renewable energy source, mainly in countries like China and Japan. The implementation of AI-powered analytics has tested to be worthwhile in optimizing the management of water float within hydropower plants. This technology ensures a constant provide of electricity while simultaneously minimizing the poor have an effect on on aquatic ecosystems. Furthermore, AI-driven building management structures are gaining recognition throughout Asia-Pacific nations. These systems leverage real-time records and occupancy patterns to effectively manipulate lighting, heating, and cooling in each industrial and residential buildings. This not solely reduces electricity consumption however also results in price financial savings on electricity payments for consumers.
The major players operating in the global artificial intelligence in renewable energy market share diversified into Alpiq, AppOrchid Inc., ATOS SE, Enel Green Power, Enphase Energy, Flex Ltd., General Electric, Origami Energy Ltd., Siemens AG, and Vestas.
Artificial intelligence in renewable energy involves the application of AI techniques, such as computer mastering and facts analytics, to improve the generation, storage, distribution, and consumption of renewable electricity sources. It encompasses a wide range of technologies, including predictive modeling, optimization algorithms, and independent manage systems, to maximize the utilization of renewable resources like solar, wind, and hydropower.
AI-driven predictive modeling can accurately forecast solar irradiance and climate patterns, enabling better energy technology forecasts. AI helps optimize the positioning of photo voltaic panels and trackers to seize most sunlight at some stage in the day, for that reason improving the average efficiency of solar strength systems.
Wind farms gain notably from AI applications. Machine studying algorithms analyze sizeable amounts of records to predict wind patterns and turbine performance. This approves for higher turbine protection scheduling and greater unique energy generation. AI helps in the management of electricity distribution grids. Machine mastering algorithms analyze real-time facts to predict power demand and pick out practicable faults or disturbances in the grid. Artificial brain permits grid operators to respond proactively, making sure a stable and reliable furnish of renewable power to consumers.
AI has been employed to protect birds and bats. Advanced pc vision structures can realize the presence of birds close to mills and briefly shut them down to forestall collisions. This modern method addresses ecological issues related with wind farms. AI is instrumental in enhancing the resilience of strength grids. Artificial intelligence structures can additionally predict and mitigate doable cyberattacks, making sure the integrity and security of renewable power infrastructure.
Artificial intelligence is being used to improve desalination approaches powered by means of renewable energy. AI algorithms optimize the operation of desalination plants, decreasing electricity consumption and charges whilst increasing freshwater production. AI is employed in the plan and optimization of floating photo voltaic farms on our bodies of water. These farms are rising as a sustainable solution for land-constrained regions, and AI helps decide the most efficient layout and protection strategies.
The artificial intelligence in renewable energy market growth is segmented into deployment type, component type, end-use industry, and region. On the basis of deployment type, the market is bifurcated into on-premises and cloud. On the basis of component type, the market is divided into solution, and service. On the basis of end-use industry, the market is classified into energy generation, energy transmission, energy distribution, and utilities. On the basis of region, the market is studied across North America, Europe, Asia-Pacific, and LAMEA.
By deployment type, on-premises is the fastest-growing segment in the artificial intelligence in renewable energy industry in 2022. AI systems can analyze real-time records from on-premises renewable energy sources like solar panels and wind turbines. They can optimize energy production by means of adjusting the perspective of photo voltaic panels, the orientation of wind turbines, and battery storage usage to maximize power technology and consumption. AI models can furnish correct strength production forecasts primarily based on climate conditions, historical data, and different factors. This helps on-premises customers design their power consumption and storage greater effectively.
Based on component type, service is the fastest-growing segment in the AI in renewable energy market in 2022. AI-based grid administration systems can stability the intermittent nature of renewable strength sources. These structures use real-time statistics to control energy distribution efficiently, ensuring a secure supply. AI can optimize electricity storage systems, such as batteries. It can determine when to store excess energy and when to release it, making the most of renewable energy production.
Based on end-use industry, energy distribution is the fastest-growing segment in the AI in renewable energy market in 2022. AI can help manage energy storage systems, such as batteries. Machine gaining knowledge of algorithms can optimize the charging and discharging of batteries primarily based on grid demand and renewable strength availability, maximizing the use of clean power and reducing reliance on fossil fuels. AI can play a crucial role in improving the resilience of strength grids in opposition to disruptions, including those brought about by using extreme climate events. Predictive maintenance algorithms can discover attainable issues in the grid infrastructure earlier than they lead to outages.
Region-wise Asia-Pacific is the fastest-growing and dominated in the market in 2022. In the Asia-Pacific region, hydropower performs a good sized role as a renewable energy source, mainly in countries like China and Japan. The implementation of AI-powered analytics has tested to be worthwhile in optimizing the management of water float within hydropower plants. This technology ensures a constant provide of electricity while simultaneously minimizing the poor have an effect on on aquatic ecosystems. Furthermore, AI-driven building management structures are gaining recognition throughout Asia-Pacific nations. These systems leverage real-time records and occupancy patterns to effectively manipulate lighting, heating, and cooling in each industrial and residential buildings. This not solely reduces electricity consumption however also results in price financial savings on electricity payments for consumers.
The major players operating in the global artificial intelligence in renewable energy market share diversified into Alpiq, AppOrchid Inc., ATOS SE, Enel Green Power, Enphase Energy, Flex Ltd., General Electric, Origami Energy Ltd., Siemens AG, and Vestas.
Key Market Insights
- Cloud was the largest revenue generator in 2022, comprising over half of the market share in 2022.
- The solutions segment is dominated by component type, contributing to almost two-thirds of the market share and showing a 32.0% Compound Annual Growth Rate (CAGR) in 2022.
- In terms of end-use industries, utilities held the top position, with a 22.8% CAGR in the market.
- The Asia-Pacific region led in revenue contribution, representing two-thirds of the global market share in 2022.
Key Benefits For Stakeholders
- This report provides a quantitative analysis of the market segments, current trends, estimations, and dynamics of the artificial intelligence in renewable energy market analysis from 2022 to 2032 to identify the prevailing artificial intelligence in renewable energy market opportunities.
- The market research is offered along with information related to key drivers, restraints, and opportunities.
- Porter's five forces analysis highlights the potency of buyers and suppliers to enable stakeholders make profit-oriented business decisions and strengthen their supplier-buyer network.
- In-depth analysis of the artificial intelligence in renewable energy market scope assists to determine the prevailing market opportunities.
- Major countries in each region are mapped according to their revenue contribution to the global market.
- Market player positioning facilitates benchmarking and provides a clear understanding of the present position of the market players.
- The report includes the analysis of the regional as well as global artificial intelligence in renewable energy market trends, key players, market segments, application areas, and market growth strategies.
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Key Market Segments
By Deployment Type
- On premises
- Cloud
By End-Use Industry
- Energy Generation
- Energy Transmission
- Energy Distribution
- Utilities
By Component Type
- Solution
- Service
By Region
- North America
- U.S.
- Canada
- Mexico
- Europe
- Germany
- France
- UK
- Spain
- Italy
- Rest of Europe
- Asia-Pacific
- China
- Japan
- India
- South Korea
- Australia
- Rest of Asia-Pacific
- LAMEA
- Brazil
- South Africa
- Saudi Arabia
- Rest of LAMEA
- Key Market Players
- Enphase Energy
- Flex Ltd.
- Vestas
- Origami
- App Orchid
- Enel Spa
- Atos SE
- Siemens AG
- General Electric
- Alpiq Holding Ltd.
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Table of Contents
CHAPTER 1: INTRODUCTION
CHAPTER 2: EXECUTIVE SUMMARY
CHAPTER 3: MARKET OVERVIEW
CHAPTER 4: ARTIFICIAL INTELLIGENCE IN RENEWABLE ENERGY MARKET, BY DEPLOYMENT TYPE
CHAPTER 5: ARTIFICIAL INTELLIGENCE IN RENEWABLE ENERGY MARKET, BY COMPONENT TYPE
CHAPTER 6: ARTIFICIAL INTELLIGENCE IN RENEWABLE ENERGY MARKET, BY END-USE INDUSTRY
CHAPTER 7: ARTIFICIAL INTELLIGENCE IN RENEWABLE ENERGY MARKET, BY REGION
CHAPTER 8: COMPETITIVE LANDSCAPE
CHAPTER 9: COMPANY PROFILES
List of Tables
List of Figures
Executive Summary
According to the report, the artificial intelligence in renewable energy market size was valued at $578.10 million in 2022, and is estimated to reach $4.6 billion by 2032, growing at a CAGR of 23.2% from 2023 to 2032.Renewable energy encompasses energy generated from naturally occurring sources like sunlight, wind, and water, which are naturally replenished over time. The integration of artificial intelligence in renewable energy market analysis involves using AI algorithms to enhance the efficiency, reliability, and sustainability of renewable energy sources. Data Availability is a fundamental factor driving the rapid integration of Artificial Intelligence (AI) into renewable energy systems. The growing availability of data from renewable energy sources has revolutionized the way these systems operate and has paved the way for more accurate predictions and optimizations through AI algorithms.
Artificial intelligence in renewable energy market opportunities represent two of the most promising technological advancements of our time. AI, with its capacity for data analysis and automation, has emerged as a powerful tool to optimize renewable energy sources. AI can balance energy supply and demand in real-time, enabling the integration of intermittent renewable sources like wind and solar into the power grid more effectively.
Renewable energy systems, such as solar panels and wind turbines, generate vast amounts of data regarding their performance, energy production, and environmental conditions. In the past, accessing and processing this data was a challenge in AI in renewable energy makret. However, with the advent of sophisticated sensors, IoT (Internet of Things) devices, and data collection platforms, renewable energy systems now generate a wealth of real-time data that can be harnessed for optimization.
Environmental concerns are a driving force behind the integration of artificial intelligence in renewable energy market forecast years. There has been a significant increase in global awareness of climate change and the environmental challenges associated with traditional fossil fuel-based energy sources. This heightened awareness has led to a growing demand for cleaner and more sustainable energy alternatives, which is where renewable energy comes into play.
Artificial intelligence in renewable energy market growth covers sources such as solar, wind, and hydropower offer a cleaner and more environmentally friendly way to generate electricity. However, they come with their own set of challenges, such as intermittency and variability. This is where AI enters the picture as a solution.
Artificial intelligence in renewable energy market scope process vast amounts of data and make real-time decisions, is instrumental in addressing the reliability and efficiency issues associated with renewable energy sources. AI-powered systems can predict and manage fluctuations in energy production from sources like wind and solar, ensuring a stable and consistent power supply. This reduces the need for backup fossil fuel-based power generation, which is a significant contributor to greenhouse gas emissions.
One of the keyways of AI in renewable energy market contributes to environmental sustainability in the renewable energy sector is through grid optimization. AI algorithms can analyze weather patterns, energy demand, and the availability of renewable resources to make intelligent decisions about when and where to generate and distribute energy. This means that renewable energy can be harnessed to its full potential, reducing the reliance on fossil fuels and minimizing the environmental impact of energy production.
The artificial intelligence in renewable energy market share is segmented into deployment type, component type, end-use industry, and region. On the basis of deployment type, the market is bifurcated into on-premises and cloud. On the basis of component type, the market is divided into solution, and service. On the basis of end-use industry, the market is classified into energy generation, energy transmission, energy distribution, and utilities. On the basis of region, the market is studied across North America, Europe, Asia-Pacific, and LAMEA.
On the basis of component type service segment is the fastest growing segment in the artificial intelligence in renewable energy market trends during the forecast period. AI-powered sensors and drones are employed for the monitoring and maintenance of renewable energy infrastructure. They can detect anomalies and issues in real time, reducing downtime and maintenance costs. AI can manage microgrids efficiently by determining when to draw energy from renewables, storage, or the main grid, ensuring reliability and cost-effectiveness. AI can also assess the environmental impact of renewable energy projects, helping in site selection and design to minimize ecological disruption.
In addition, the report covers profiles of key artificial intelligence in renewable energy industry participants such as Alpiq, AppOrchid Inc., ATOS SE, Enel Green Power, Enphase Energy, Flex Ltd., General Electric, Origami Energy Ltd., Siemens AG, and Vestas.
Key Market Insights
In terms of development type, cloud segment dominated and on premises segment is the fastest-growing market representing a CAGR of 23.0% and 23.4% respectively during the forecast period.By component type, the service is the fastest-growing segment the in artificial intelligence in renewable energy market in 2022 representing the growth of 23.4% of the CAGR in 2022.
By end-use industry, the utilities segment dominated the artificial intelligence in renewable energy market in 2022 representing a growth of 22.8% of CAGR during the forecast period.
By region, Asia-Pacific was the highest revenue contributor, growing with a CAGR of 23.6%.
Companies Mentioned
- Enphase Energy
- Flex Ltd.
- Vestas
- Origami
- App Orchid
- Enel Spa
- Atos SE
- Siemens AG
- General Electric
- Alpiq Holding Ltd.
Methodology
The analyst offers exhaustive research and analysis based on a wide variety of factual inputs, which largely include interviews with industry participants, reliable statistics, and regional intelligence. The in-house industry experts play an instrumental role in designing analytic tools and models, tailored to the requirements of a particular industry segment. The primary research efforts include reaching out participants through mail, tele-conversations, referrals, professional networks, and face-to-face interactions.
They are also in professional corporate relations with various companies that allow them greater flexibility for reaching out to industry participants and commentators for interviews and discussions.
They also refer to a broad array of industry sources for their secondary research, which typically include; however, not limited to:
- Company SEC filings, annual reports, company websites, broker & financial reports, and investor presentations for competitive scenario and shape of the industry
- Scientific and technical writings for product information and related preemptions
- Regional government and statistical databases for macro analysis
- Authentic news articles and other related releases for market evaluation
- Internal and external proprietary databases, key market indicators, and relevant press releases for market estimates and forecast
Furthermore, the accuracy of the data will be analyzed and validated by conducting additional primaries with various industry experts and KOLs. They also provide robust post-sales support to clients.
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