However futuristic it may appear, the assets that enable renewable energy consumption – like wind turbines, solar farms, and battery storage facilities – are still imperfect machines. And like any other machine, they are vulnerable to defects, downtime, and similar disruptions – especially due to their positioning outdoors. These challenges can add up, at times undermining the very benefits the assets are supposed to provide.
Fortunately, another technology can help keep business' renewable energy assets humming effectively. When asset management techniques are coupled with AI, they can confront disruptions with deep insights and automation – and better harness the potential of renewable energy.
AI and reliability-centered maintenance
Asset owners in manufacturing, transportation, and oil and gas frequently leverage reliability-centered maintenance (RCM) as part of their broader asset management strategy. Renewable energy asset owners should, as well – especially since it dovetails with advances in AI.
RCM is about maximizing the uptime and productivity of assets using carefully customized plans. It is a smarter alternative to more dated approaches, like reactive maintenance or run-to-failure. RCM is also about precision. Rather than applying a blanket strategy to every solar panel and wind turbine, asset owners tailor RCM to each individual machine, its component parts, and the unique threats and risks to its health.
When AI is threaded into the fabric of RCM, it can supercharge the benefits. AI-powered RCM can significantly lower maintenance costs: Automated tools and processes monitor assets and parts in near real-time, detect problems and failure probabilities, and immediately act by generating work orders and root cause recommendations for technicians. AI-powered RCM can also reduce environmental impact: Detailed asset data parsed by machine learning algorithms ensure components are replaced at precisely the right time, rather than prematurely or too late. This means longer asset lifecycles, less consumption, fewer strains on supply chains, and a smaller carbon footprint.
AI and field service management
Renewable energy asset owners do not have the luxury of all their machines being under one roof – or any roof at all. Unlike in manufacturing plants, renewable energy assets are often far flung. This requires a field service management approach: The ability to monitor and manage assets spread across a large geography. But field service management comes with a set of logistical challenges that can hinder renewable energy asset owners, like complicated scheduling, dispatching, and mobile access. When these challenges compound, they can sap productivity and savings and undercut a renewable energy asset's value.
Fortunately, AI and other emerging technologies are mitigating these complexities. Strategic data collection and curation can optimize technicians' time, providing insights into the best worker for the job based on location, start time, and expertise. These same tools can also automate the creation and assignment of work orders. Once technicians arrive on the job, AI-powered applications on their mobile device can even recommend the necessary repairs based on historical data and then walk technicians through the process. The snapshot of all this? A notable increase in first-time fix rates and technical productivity.
Renewable energy assets can be notoriously difficult to monitor and manage. They are complicated, sprawl across diverse terrain, and are by their very nature exposed to elements like weather and debris. Fortunately, the right asset management techniques coupled with technology like machine learning and generative AI can help overcome these difficulties. The potential is not science fiction: IBM, worked with Param Renewables in India to manage six gigawatts of renewable assets using AI-powered technology – and reduce controllable launches by 25% across tens of thousands of solar panels and dozens of inverters. Asset owners must leverage today's latest tools to truly maximize their assets' value.
ABOUT THE AUTHOR: Kendra DeKeyrel is a vice president in IBM's automation and sustainability software division, specializing in solutions that drive efficiency, reduce environmental impact, and create value for organizations around the globe.
