The low-carbon transition will need AI to integrate a large increase in intermittent renewable energy while ensuring a stable grid.

In brief

• Artificial intelligence (AI) has the ability to unlock the vast potential of renewables. Failure to embrace it means risking falling behind the curve.


• The powerful prediction capabilities of AI will lead to improved demand forecasting and asset management.


• The automation capability of AI can drive operational excellence in many crucial areas.

The energy sector faces pressing challenges and needs to act with urgency. Policy commitments to a net-zero future, such as the Paris Agreement, mean the transformation to a low-carbon economy must come at pace.

Major disruption to the electricity sector is on the cards as governments ramp up renewables and transition away from fossil fuels. While renewable energy looks set to flourish amid this backdrop, its intermittent nature means solutions will need to be found to keep grids stable. Additionally, the industry is changing from a Industry based on commodity pricing to a Industry based on technology solutions in order to integrate renewable energy. As the energy industry continues to utilize more variable generation sources, accurate forecasts of power generation and net load are becoming essential to maintain system reliability, minimize carbon emissions and maximize renewable energy resources.

As we move into the Fourth Industrial Revolution, grid operators, developers and consumers are harnessing artificial intelligence (AI), paving a path for a smooth transition to a greater use of renewables. AI's ability to provide better prediction capabilities is enabling improved demand forecasting and asset management, while its automation capability is driving operational excellence - leading, in turn, to competitive advantage and cost-savings for stakeholders.

Supported by other emerging technologies, such as the internet of things (IoT), sensors, big data and distributed ledger technology, AI has the ability to unlock the vast potential of renewables. Failure to embrace it would leave the renewable energy sector falling behind.

AI is far superior to humans when it comes to carrying out complex tasks at speed. Given that an energy grid is one of the most complex machines ever built and requires split-second decisions to be made in real time, AI algorithms are a perfect fit.

How AI is transforming renewable energy

• From demand forecasts to asset maintenance, the application of AI could bring gains on many fronts.

As an increasing amount of megawatts feeds into the grid from variable renewable energy sources, predicting capacity levels has become paramount to secure a stable and efficient grid. This is due to the fact that with renewables taking up a greater share of the grid, there is a loss of baseload generation from sources such as coal, which provide grid inertia via the presence of heavy rotating equipment such as steam and gas turbines. Without grid inertia, power networks will be unstable and susceptible to blackouts. Now, with the application of sensor technology, solar and wind generation can provide an enormous amount of real-time data, allowing AI to predict capacity levels.

Before harnessing AI, most forecasting techniques relied on individual weather models that offered a narrow view of the variables that affect the availability of renewable energy. Now, AI programs have been developed - such as IBM's program for the US Department of Energy's SunShot Initiative - which combine self-learning weather models, datasets of historical weather data, real-time measurement from local weather stations, sensor networks and cloud information derived from satellite imagery and sky cameras.

The result has been a 30% improvement in accuracy in solar forecasting, leading to gains on multiple fronts. "We found that improved solar forecasts decreased operational electricity generation costs, decreased start and shutdown costs of conventional generators, and reduced solar power curtailment," says Hendrik Hamann, Distinguished Researcher and Chief Scientist for Geoinformatics at IBM.

Forecasts of the base variables - wind speed and global horizontal irradiance, as well as the resulting power output - allows for a view on a range of time horizons, from minutes and hours ahead (for maintaining grid stability and dispatching resources) to day-ahead (optimizing plant availability), to several days ahead (scheduling maintenance).

With increasingly larger data sets becoming available, predictions can now go far beyond the weather to train algorithms to predict more remarkable outcomes. For instance, how much additional power is used during a festive holiday, a large-scale international event, or how much altitude impacts a community's energy use.

For generators and energy traders, more accurate forecasting of variable renewable energy at shorter timescales allows them to better forecast their output and to bid in the wholesale and balancing Industrys - and, importantly, to do so while avoiding penalties.

"The earlier and more accurately you can predict, the more efficient it is for energy traders to rebalance their position. I see AI providing a way of dealing with lots more sites and using more granular and diverse data than historic forecast methods," says Alex Howard, Head of Strategy at Origami. "Ultimately, that means making a better financial return."

The global artificial intelligence (AI) in renewable energy Industry size was exhibited at USD 10.20 billion in 2023 and is projected to hit around USD 117.72 billion by 2033, growing at a CAGR of 27.71% during the forecast period of 2024-2033.

Key Takeaways:

• Asia-Pacific dominated the Industry with largest share in 2023.


• North America is expected to develop at the fastest rate during the forecast period.


• The demand forecasting segment led the Industry in 2023 with highest revenue share.


• The energy generation segment held largest revenue share in 2023.

Some of the prominent players in artificial intelligence (AI) in renewable energy Industry include: Alpiq, SmartCloud Inc., General Electric, Siemens AG, Hazama Ando Corporation, ATOS SE, AppOrchid Inc., Zen Robotics Ltd., Origami Energy Ltd., and Flex Ltd.

Growth Factors

One of the key factors driving the growth of global artificial intelligence (AI) in renewable energy Industry is growing demand for electricity in emerging and established regions. The growing need for smarter energy grids is also driving the growth and development of artificial intelligence (AI) in renewable energy in the global Industry. Another factor that is contributing towards the growing demand of artificial intelligence (AI) in renewable energy sector is digitalization of the energy sector.

Artificial intelligence has helped to identify the bottlenecks of the energy sector. This factor is opening new opportunities for the growth of global artificial intelligence (AI) in renewable energy Industry. Artificial intelligence uses vast amount of data and information for analyzing and streamlining of processes in renewable energy sector. All of these aforementioned factors are driving the growth of global artificial intelligence (AI) in renewable energy Industry during the forecast period.

The expansion of global artificial intelligence (AI) in renewable energy Industry is being propelled by growing demand for renewable energy. The growing adoption of artificial intelligence based smart home solutions is predicted to promote artificial intelligence adoption in the renewable energy sector throughout the forecast period. Artificial intelligence solutions in the renewable energy industry are also being driven by the growing demand to cut electricity prices and energy wastage.

Furthermore, the rising government initiatives in developed and developing nations for the expansion of renewable energy sector are also boosting the growth of global artificial intelligence (AI) in renewable energy Industry. Moreover, government is also collaborating with major Industry players for the development of artificial intelligence (AI) in renewable energy Industry. The artificial intelligence (AI) in renewable energy Industry is also getting high investments by government as well as Industry players.

The growing importance for clean and green energy is boosting the growth of artificial intelligence (AI) in renewable energy Industry over the forecast period. In addition, rising carbon emissions and greenhouse gases emissions are also boosting the growth of renewable energy sector. The renewable energy helps in the reduction of toxic gases in the environment. Thus, the growing concerns regarding environment and surroundings are also driving the growth of renewable energy sector. This is directly impacting the growth of global artificial intelligence (AI) in renewable energy Industry.

The COVID-19 pandemic had drastic and significant impacts on the growth of renewable energy sector. This directly impacted the growth of global artificial intelligence (AI) in renewable energy Industry. The manufacturing units of energy were halted or shut down during the coronavirus outbreak. This had substantial impact on the growth and development of global artificial intelligence (AI) in renewable energy Industry during 2020.

The developed and developing regions such as Europe, North America, and Asia-Pacific are highly adopting artificial intelligence in renewable energy sector. All these regions are contributing towards the growth of global artificial intelligence (AI) in renewable energy Industry during the forecast period. In addition, the existence of major Industry players in this Industry is also boosting the growth of global artificial intelligence (AI) in renewable energy Industry. The number of strategies is being adopted by Industry players operating in artificial intelligence (AI) in renewable energy Industry to enhance their Industry position.

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