How Artificial Intelligence is Changing Renewable Energy: Trends and Directions in 2024

AI is rapidly reshaping the renewable energy sector, making our energy systems smarter and more efficient. The pace of development is evident – in 2022, the market for AI in this field was around $10 billion, but it is expected to skyrocket to nearly $115 billion by 2032. AI is already being used in over 50 different ways in this sector!

One big reason for this surge is demand forecasting, which accounts for more than 27% of the market. AI helps energy providers predict future energy needs more accurately, optimize production, and reduce waste, which is crucial for effectively integrating renewable energy sources.

What’s really exciting to observe as well is how AI is improving our use of renewables like wind and solar. By fine-tuning how we produce and store energy, this technology makes green energy more reliable and accessible to everyone.

Sustainability is more than just a buzzword; it’s a priority for energy companies these days. In their search for technologies to support their environmental and cost-saving goals, companies can turn to AI to help improve operational capabilities. Let’s look at the key benefits and trends driving progress in this field.

AI can predict when equipment like wind turbines and solar panels need maintenance and, as a result, prevent costly breakdowns and extend usage. For example, in the case of wind turbines, AI-driven predictive maintenance can increase their lifespan by up to 10%. This means fewer resources are wasted, and energy systems can run more smoothly for longer periods.

The numbers show us just how crucial AI technologies are in reducing emissions. Did you know that optimizing energy production and use with AI could reduce global greenhouse gas emissions by 5-10% by 2030?

If your company prioritizes corporate sustainability, you know there are many fields to consider, from energy management to transportation, manufacturing processes, and supply chain operations. AI-powered technologies can help achieve significant energy savings and reduce CO2 emissions in all these areas.

One of the main areas where the growth of AI is driving up energy demand is data centers. By 2030, these centers could account for even 9% of the U.S.’s electricity generation, double the current consumption. This may sound like a negative trend, but it also opens up opportunities for renewable energy providers to supply more green power and make data centers more sustainable.

While AI offers tremendous potential, organizations face several hurdles when integrating this technology—these range from a shortage of talent to environmental and security concerns.

• Finding Skilled Professionals: There’s a significant gap between the demand for AI expertise and the number of professionals available. Many organizations struggle to find people with the right skills to develop and manage AI systems.
• Energy Demands: Running AI models, especially those that are large and complex, requires a lot of computing power, leading to high energy use. This not only drives up costs but also increases the carbon footprint.
• Impact on the Environment: AI systems’ high energy needs can also have a substantial environmental impact. To address this, companies should consider investing in greener infrastructure to reduce the negative effects on our planet.
• Understanding AI Decisions: One of the biggest challenges with AI is that it often operates in opaque ways. These “black box” models can make decisions that are hard to explain, which can create trust issues and make it difficult to ensure ethical use.
• Cybersecurity Threats: As AI solutions become more integrated into various aspects of business and society, they become attractive targets for cyberattacks. This creates a need for strong security measures to protect against potential breaches.

Here are several key use cases where AI is making a significant impact in renewable energy:

AI’s precision can’t be underestimated. It’s evident when preparing for extreme weather events and climate-related disasters and also when optimizing the performance of renewable energy sources like solar and wind farms. For instance, a better understanding of weather patterns helps predict solar energy production and wind speeds, improving the efficiency of these energy systems.

AI is used to analyze large datasets to optimize plant layouts and improve equipment performance. For example, companies like Siemens Gamesa use AI to boost the output of offshore wind farms, which helps lower costs and increase power generation. Additionally, it enables real-time management of energy supply and demand, which is essential when integrating renewable sources into the power grid.

Smart meters powered by AI give utilities a clearer picture of energy consumption. This understanding helps with more accurate demand management and flexible pricing. On the maintenance front, AI is a valuable tool for predicting when equipment might fail, as analyzing data from various sensors can forecast potential issues before they occur. It helps prevent unexpected breakdowns and keeps operations running without a hitch.

Utilities are now more adept at handling energy demand and supply since AI-powered systems forecast peak usage times and adjust energy distribution. AI also helps improve customer service by offering personalized energy-saving tips and simplifying billing, so managing energy use and costs becomes simpler for everyone. For example, ABB’s AI-enabled demand forecasting tool allows commercial building managers to avoid peak charges and take advantage of time-of-use tariffs.

Accurate predictions are crucial for balancing grid demand, and AI excels in this area. By forecasting when and where energy will be needed, AI allows utilities to adjust supply in real-time. This is particularly important for handling renewable energy sources, which can be unpredictable due to changing weather conditions. Thanks to AI, the grid stays balanced, outages are less likely, and there’s always a steady flow of energy, even when renewable sources aren’t working at full capacity.

AI also plays a role in educating consumers about their energy use. It generates detailed reports on energy consumption and offers practical tips for improving efficiency, helping individuals make informed decisions about their energy habits. Moreover, the data collected by AI can be used by utility companies and governments to promote energy conservation and sustainable practices and raise awareness about responsible energy use.

Fusion Energy: AI and machine learning are speeding up progress in fusion energy, a promising form of clean energy that aims to replicate the power generation process occurring in stars. Researchers can simulate the complex processes inside fusion reactors, which helps them refine designs and control systems much faster than traditional physical experiments would allow.

Carbon Capture: Research from the University of Surrey showed that applying AI to a carbon capture plant model improved CO2 capture by 16.7% and cut energy use from the grid by 36.3%. This highlights AI’s potential in supporting efforts to cut greenhouse gas emissions.

While the world of renewable energy is evolving rapidly, we witness some exciting tech innovations making a real impact. From optimizing solar and wind power to improving energy storage and investment, here’s how different companies are making a difference.

Engie is making strides in renewable energy with AI, using data from solar and wind farms to fine-tune performance and improve maintenance. By blending this with weather forecasts, satellite images, and maps, they can also pinpoint the best spots for new solar and wind farms based on optimal sunlight and wind exposure.

Nvidia also plays a vital role in this space, mainly through edge computing. For example, Noteworthy AI has equipped utility trucks with smart cameras powered by Nvidia’s Jetson Xavier NX. These cameras capture high-resolution images of utility poles and equipment, improving data collection on the go. Processing these images right on the truck saves time and money, while onboard AI helps quickly identify poles and manage data.

TotalEnergies’ Solar Mapper tool speeds up solar potential assessments. Thanks to machine learning and data from Google Cloud, what used to take days or weeks can now be done in moments. This tool covers 90% of France and is crucial for TotalEnergies’ mission to become carbon-neutral by 2050 and boost global solar energy adoption.

Amazon, meanwhile, is using AI to make solar energy more reliable by pairing it with battery storage. At the Baldy Mesa solar farm, for instance, AI models powered by AWS help predict when to store and release energy so the power keeps flowing even after the sun goes down. This approach ensures a steady supply of clean energy and supports Amazon’s commitment to sustainability.

Finally, Nithio is working to make clean energy more accessible with its AI-driven platform, Nithio FI. They analyze data on geography and repayment patterns to simplify investment risks and channel funding to both large solar companies and local operators. With $26 million in play and a goal to surpass $100 million by 2025, Nithio is driving significant growth in the solar sector.

Scalo is here to make a real difference in the renewable energy world. Imagine having a system that handles energy management with a level of automation and insight that reduces waste and boosts efficiency. That’s what we offer. Our tools are designed to make managing energy straightforward so you can focus on what you do best.

For those in solar and wind energy, we provide tailored solutions that help you manage everything from generation to storage. Our software ensures you get the most out of your resources and keep operations seamless.

At Scalo, we’re not just about providing technology; we’re about ensuring it fits your needs and helps your business grow. Our team is here to support you every step of the way.