Deploy, deploy, deploy.

That has been the mantra in solar development, and deploying quickly and efficiently remains a principal goal. But all that deployment has built some 142,000 utility-scale, 20,000 commercial, and 7,000 community-solar installations in the United States alone. And installed capacity grows with each passing day.

That’s great news. But while solar power is far lower maintenance than the fossil-energy plants they supplant, they aren’t maintenance-free. Storms damage panels. Inverters fail. Critters nibble on wires. Dust collects on panels and vegetation shades them. And solar farms can be in far-flung locations where land is cheap and skilled labor scarce.

For all those reasons, optimizing operations & maintenance (O&M) is a hot topic in solar. Artificial intelligence — and, in particular, generative AI (GenAI) — is a hot topic everywhere. It turns out that AI is already making a difference in solar O&M, and its role will only grow as the technology advances and solar firms corral their data in centralized, cloud-based databases in which AI thrives. In the meantime, here are five ways to apply AI in making solar O&M more effective and efficient.

1) AI to improve jobsite safety in solar O&M

GenAI can be a boon to jobsite safety. It can take data from diverse sources — job-scheduling information, weather forecasts, site maps, incident reports and whatever else may be relevant — and distill it into safety notices pushed to workers’ mobile devices as they arrive onsite. It might tell them to keep an eye out for rattlesnakes, or that there’s serious risk of lightning midafternoon, or that steep grades have sprained ankles in the past — or all of the above.

Without AI, accessing all that involves tapping through enough screens on discrete applications (a work-order app, a safety app, a forms app…) that field techs may give up and take their chances — there’s work to do, after all. GenAI can integrate real-time data from multiple sources to make safety information immediately accessible, clear and tailored to location. Such a system can pay off by keeping scarce field resources healthy and on the job, and it shows workers that you care about their wellbeing.

2) AI for summarizing solar-asset status and history

Solar installations can involve of tens of thousands of individual assets, each catalogued and, as time passes, its maintenance and repair history chronicled. Field techs want to know whether and how a particular asset has broken or failed in the past — or if similar assets have failed elsewhere — and whether the parts for fixes are in inventory. GenAI can distill asset history, maintenance logs, spare-parts inventory, technical-manual instructions and other information into succinct, readable formats. That avoid techs having to dig through scads of information across multiple sources — assuming they bother at all. And not bothering can mean reinventing the wheel, and that’s not efficient, either.

3) AI computer vision and object detection to sharpen and speed solar maintenance and repairs

Computer vision and object detection can play important roles in solar O&M. It can guide vegetation management to areas of overgrowth. On the asset level, it can confirm whether the piece of hardware a tech is poised to work on is in fact the one that needs attention. It can also identify whether that asset has corroded or has been damaged.

Some of the more exciting computer vision applications involve GenAI. That combination enables real-time repair instructions based on the problem immediately at hand and image-based verification that a repair was done correctly (i.e., if there are supposed to be four screws in a panel and the system detects only two, it lets the tech know). That prevents revisits and connectivity with core systems means infrastructure managers can see that the work was done right the first time.

Augmented reality (AR) will play a role here. Assuming connectivity and the development of affordable, field-grade AR hardware, one envisions wearables verbally or visually walking techs through repairs.

4) AI to digitize Service Level Agreements (SLAs) and improve dispatching

Scheduling and dispatching solar O&M personnel is an exercise in three-dimensional chess. Among the key variables include technician skills, availability, location and schedule on one hand and jobsite locations and the nature of the O&M tasks and priorities on the other.

Figuring out rational schedules based on multivariate inputs is a strength of machine learning, and that’s also the case in solar O&M. But GenAI has a special role to play here, and that’s in assessing digitized SLAs to help ensure that O&M tracks with agreements.

That input can be decisive in dispatching. All logic may point to servicing installation A, but if installation B also has an issue, and B’s SLA stipulates a more pressing turnaround time, B may get priority. Digitized SLA data that GenAI can access can automate such decisions.

5) AI agents to make life easier and improve throughput in the control center

The jobs of solar control-center staff look a lot like those of air traffic controllers. Anything you can take off their plates is a win. AI agents can do that — specifically, by automating O&M requests and approvals with third-party service providers.

An AI agent can reach out via text-, email- or voice-based on the preference of the service provider. The AI agent tracks approvals, contacts alternate O&M providers if primary contacts don’t come through and follows up. It only alerts human staff if it dead-ends. In a busy control room, AI agents can offload tedious work and enable the management of more assets with fewer people.

The common denominator: centralized data, in the cloud

AI is already making solar O&M safer and more efficient, and there’s much more to come. Whether or not solar firms incorporate AI in the near term, they should be moving their IT infrastructure to cloud platforms that enable the centralization of business and operational data. That’s going to be indispensable to exploiting AI innovations in O&M and beyond going forward.