When a commercial solar system underperforms, the cost is not only the lost kilowatt-hours but also the time it takes to find out why. This case study describes how a commercial and industrial (C&I) solar site in California used ClearSpot to detect the underperformance affecting the site and moved from about three weeks of guessing to real-time visibility.
In short: ClearSpot is an AI and drone automation platform for solar O&M that combines monitoring analytics, drone and AI inspection and automated work orders. At a C&I solar site in California, ClearSpot detected the underperformance affecting the site, replacing roughly three weeks of manual investigation with real-time visibility into how the system was performing.
Seeing unexplained drops in output? Book a demo to see how ClearSpot finds underperformance on your sites.
Case summary
| Item | Detail |
|---|---|
| Customer | A commercial and industrial (C&I) solar site in California (anonymised) |
| Challenge | Underperformance with no clear cause; about three weeks of manual investigation |
| Approach | ClearSpot automated solar operations platform |
| Result | ClearSpot detected the underperformance; the team moved from about three weeks of guessing to real-time visibility |
Why speed matters for California C&I solar
Many California commercial customers are billed on time-of-use (TOU) electricity rates, where the price of grid power depends on the time of day. For a business with on-site solar, that means not every lost kilowatt-hour is worth the same. Production lost during higher-priced peak hours has to be replaced with more expensive grid power, so an issue that cuts output at those times costs more than the same loss at off-peak times.
The practical consequence is simple: the longer underperformance goes unnoticed or unexplained, the more peak-hour value it can erode. Finding it quickly matters as much as finding it at all. For a broader view of how missed faults turn into lost revenue, see the real cost of a missed solar fault.
The challenge: three weeks of guessing
The site’s output was lower than it should have been, but the cause was not obvious. Investigating a problem like this by hand typically means pulling data from monitoring portals, comparing it against expectations, checking equipment and ruling out possible causes one by one. At this site, that process took about three weeks.
This is a common situation for C&I sites, which often have limited on-site staff and rely on periodic checks rather than continuous analysis. Typical obstacles include:
- Monitoring dashboards show that output is down but not why.
- Expected yield is hard to calculate reliably without trustworthy irradiance and temperature data.
- Faults at module or string level do not always show clearly in inverter-level data.
- Each investigation step requires someone to collect and compare data manually.
Our article on why string monitoring alone is not enough covers this gap in more detail.
Data gaps that slow diagnosis and how ClearSpot closes them
At many C&I sites, the underlying data has gaps that slow diagnosis: interrupted inverter or monitoring data, inconsistent naming between portals, sensor readings that make expected-yield figures unreliable, and inspection results that are not linked to specific equipment.
ClearSpot closes these gaps by flagging missing or suspect data automatically, normalising data into one asset model, using drone thermal and RGB inspection to cover what monitoring cannot see, cross-checking expected against actual yield, and linking every finding to an asset and a work order.
The approach: continuous solar underperformance detection
The site was connected to the ClearSpot automated solar operations platform, which replaces manual investigation with continuous analysis. The platform’s main capabilities for this kind of problem are described below.
Expected vs actual yield
ClearSpot compares actual production with expected production for the conditions on site, so a shortfall is detected as it happens rather than at the end of a billing or reporting period.
Monitoring analytics
Operational data is analysed continuously to narrow down where a loss is coming from, rather than leaving the team to search across dashboards. See our guide to AI solar performance monitoring for how this works.
Drone and AI inspection
Where monitoring cannot see the cause, drone thermal and visual inspection with AI analysis can locate faults at module level, so the finding points to specific equipment.
The result: from guessing to real-time visibility
ClearSpot detected the underperformance affecting the site. Instead of spending about three weeks investigating by hand, the team now has real-time visibility into how the system is performing and where losses come from. Teams that want to test the same change on their own sites can agree detection lag and other KPIs for a ClearSpot pilot before it starts.
For a C&I site on time-of-use rates, that shift matters because issues can be found and acted on before they erode more peak-hour value. It also changes the team’s work from searching for problems to fixing them. Teams managing commercial portfolios can read more in our C&I solar asset management overview.
ClearSpot results at a glance
Beyond this case, these are the outcomes ClearSpot reports across its deployments:
| Metric | ClearSpot result |
|---|---|
| Fault detection | 99% |
| O&M cost reduction | 30–50% |
| Downtime reduction | 65% |
| Plant availability | 97%+ |
| Time to diagnose underperformance (C&I site, California) | From about three weeks of manual investigation to real-time visibility |
Frequently asked questions
What did ClearSpot do at this California C&I site?
ClearSpot detected the underperformance affecting the site and gave the team real-time visibility into system performance, replacing about three weeks of manual investigation.
Why does fast underperformance detection matter in California?
Many California commercial customers pay time-of-use electricity rates, so solar production lost during higher-priced peak hours costs more to replace with grid power. The sooner underperformance is found, the less of that value is lost.
How does ClearSpot detect solar underperformance?
It compares actual against expected yield, analyses monitoring data continuously, flags data gaps, and uses drone thermal and visual inspection with AI to locate faults that monitoring alone cannot see.
Who was the customer?
A commercial and industrial solar site in California. The customer is anonymised, so the name and site details are not published.
Does ClearSpot work for utility-scale portfolios as well as C&I sites?
Yes. The same platform is used across utility-scale portfolios, where ClearSpot reports 99% fault detection, a 30–50% reduction in O&M costs, a 65% reduction in downtime and 97%+ availability.
How can I see this on my own site?
Book a demo through the get-a-demo page and we will walk through underperformance detection using data similar to your sites.
Next step
If finding the cause of lost output takes weeks, book a ClearSpot demo to see real-time underperformance detection on your own sites.