Introduction: The challenge of economic sustainability
A few statistics make clear the impact renewable generation is having on global electricity production. From 2010 to 2024, the cost of utility-scale solar generation fell by 90%. In 2024, solar power capacity surged by 474 TWh, sufficient to handle 40% of all global electricity demand growth. Meanwhile, over on the wind power side of things, the industry is having its most successful year ever in 2026, this despite many much-publicized political headwinds. In 2025, wind power output grew by 205 TWh (for a global total of 2615 TWh), the second largest growth rate, trailing only solar. Long story short, political administrations and popular sentiment may come and go, but one thing remains unflagging: power producers will always move toward the most economical generation sources, particularly those that show every sign of continuing their rapid price declines, both to install and to operate. With wind and solar PV now the cheapest generation forms available, new renewable electricity generation and battery storage assets are certain to grow rapidly in almost every country.
AI empowers sustainable generation
On track to provide nearly half of global electricity demand by the end of this decade according to the latest report from the International Energy Agency (IEA)—despite the vagaries of politics, subsidies, and manufacturing economics—renewable owner/operators need to invest in advanced technology like artificial intelligence (AI) to meet the challenge of delivering efficient operations and maximizing the utilization of their existing asset bases. Whether it’s solar panels, wind turbines, or battery energy storage systems (BESS), the challenge is the same: keep your assets producing as reliably and economically and for as long as possible while driving maintenance costs down and ensuring utilization remains high. In years (and technologies) past, that meant lots of preemptive maintenance to hedge against the possibility of asset failure, maintenance that in many cases was actually unnecessary and incurred undue costs, both in terms of work performed and productivity lost during that work.
But with the arrival of AI-powered asset performance management (APM), this state of affairs has changed dramatically for the better, with asset owners/operators now able to identify impending failures well before they happen and take proactive steps to address such failures, conducting recommended maintenance at times least disruptive to operations and least damaging to profitability.
AI-enabled APM is predicated on two important capabilities: prediction and prescription. The former indicates when failures are going to take place and on what specific equipment, while the latter tells operators what they can do about it. Both, however, are built upon a critical foundation, i.e., the fact that assets do not continue functioning throughout their lifetimes the way they did the day they left the factory. Assets age, parts wear out, maintenance takes place, the operating environment takes its toll. This means maintenance procedures provided by original equipment manufacturers (OEMs) for new systems cannot ensure long-term performance that optimizes the operating lifetime of assets whose lives are frequently measured in decades. That’s where AI-powered predictive/prescriptive maintenance enters the picture.
AI enabled APM raises the bar on asset performance
Predictive maintenance uses AI and real-time performance data to determine the likelihood of an asset failing and the estimated time until such a failure will occur. This allows maintenance to be scheduled only when it’s required, i.e., just before a breakdown, thus avoiding costly/unnecessarily premature servicing or failing to forestall a catastrophic failure, with all its expense, productivity, and safety outcomes. In order to perform predictive maintenance, AI models are fed a continuous stream of performance data from sensors on critical pieces of equipment like pumps, engines, and turbines, including key performance indicators (KPIs) like:
- Vibration (for rotating equipment like turbine generators)
- Temperature, pressure, flow rates
- Current and power consumption
- Historical maintenance logs, images/videos
Machine learning (ML) algorithms analyze this real-time data to understand what constitutes “normal” baseline operating conditions—conditions which, critically, change significantly as equipment ages. The model then flags anomalies—subtle drifts or fluctuations—invisible to human operators but pointing to the early onset of degradation and eventual failure.
Finally, advanced AI techniques, like Normal Behavior Modeling (NBM), process these patterns and apply knowledge from historical failure cases to understand the sequence of events leading to the forthcoming failure. The model then calculates the probability of failure as well as the remaining useful life (RUL) of a system or component, providing an accurate warning, often weeks or months in advance.
Case studies in proactive renewable asset performance management
AI-enabled predictive/prescriptive maintenance techniques enable renewable power operators to identify and forego failures, increasing power output, revenue/profit generation, and employee safety.
—Jensen Huang, CEO, NVIDIA
Wind turbine pitch bearing failure prediction
A renewable energy company had previously used manual grease sample analysis to determine the operating health of blade pitch bearings on its wind turbines. This process was time consuming and required taking the turbine offline to draw samples. Using Avathon’s Autonomy for Renewables platform, the company now obtains real-time pitch bearing performance data continuously without the need to take the turbine offline or forego power generation opportunities. Impending bearing failures are identified up to one year in advance, saving up to $150K per failure event through avoided crane callouts and minimized turbine downtime.
Maximizing solar production by detecting tracker misalignment
Solar tracking system misalignment leads to hidden energy losses. Avathon Autonomy for Renewables uses AI and machine learning to analyze tracker actuator/motor data and inverter DC inputs, monitoring and optimizing solar energy yields and reducing maintenance costs. Avathon’s Autonomy platform solves the problem of monitoring and optimizing single-axis tracker alignment by automatically aggregating tracker performance data and conducting deep analysis to detect any rows that need attention. More than that, it contextualizes the production losses associated with any misalignment, increasing solar energy yields and profits.
Battery storage asset performance is key to reliable power delivery
BESS assets are critical enablers of reliable renewable power, compensating for the intermittency of wind and solar generation, increasing the ability of renewables to contribute to baseload power generation. By identifying degraded asset conditions and predicting failures, Avathon’s Autonomy platform ensure that BESS owners and operators are positioned to profitably and reliably store and deliver energy by proactively identifying challenging operational issues and providing the information needed to remedy maintenance issues and ensure reliable, efficient system performance.
The benefits of AI-powered renewable operations are immense
Avathon’s Autonomy Platform delivers tangible, measurable benefits across the entire lifecycle of your renewable energy portfolio.
Revenue Enhancement: By ensuring assets are always performing optimally, the platform maximizes the amount of energy generated and sold. Accurately forecasting energy prices and grid conditions enables autonomous, profit-maximizing decisions on when to sell energy or adjust output.
Operating Efficiency: The platform eliminates bottlenecks by automating routine processes, freeing up human operators to focus on more complex, strategic initiatives.
Cost Reduction: Autonomous scheduling and predictive maintenance reduce the cost of repairs and inspections, ensuring maintenance teams are deployed only when and where they are needed, with the right parts and tools.
Lower Unplanned Downtime: By anticipating failures, organizations can schedule maintenance proactively, leading to significant reductions in unplanned downtime.
Extended Asset Lifespan: Proactive/targeted repairs reduce unnecessary strain on assets, extending lifetimes.
Conclusion: The key to renewable portfolio performance is autonomous
Asset-intensive industries like electric power generation (renewable and traditional) rely on uninterrupted performance to ensure the financial health of their operations and organizations. Failures—particularly unplanned ones—reduce energy outputs, compromise service reliability, put employees at risk, and drive down financial performance. Predictive/prescriptive maintenance helps industry managers and executives keep assets operating efficiently and economically, maximizing asset lifetimes, and extracting the greatest value from their fleets.
The greatest challenge to achieving high levels of renewable generation asset performance is ensuring that the actions taken to maintain assets are timely and accurate. Companies need to integrate data from all their available renewable assets and apply AI-powered capabilities like the Avathon Autonomy Platform to ensure value-creating outcomes.
With the accelerating global growth rate of renewables, the relationship between AI and operational performance is growing in importance with each passing year. As the world’s energy needs continue to grow, leveraging the power of AI will be key to ensuring that the planet’s future energy needs are met in a sustainable and economic manner.
Visit our site to learn more about how Avathon’s Autonomy Platform for Renewables can improve your operational performance and lead to greater reliability and profitability. Click here to download our white paper on the Autonomy Platform for Renewables.
