As solar portfolios grow, the opportunity to improve performance gets bigger and bigger.
Recent Raptor Maps data reveals equipment-driven losses for solar assets climbed from on the order of 1-2% to >5% over the last 5 years, showing how much performance is driven by distributed, system-level faults throughout the plant. These tend to be tiny issues that are difficult to notice and harder to act on fast.
This is a clear opportunity to increase both the amount of physical operational data accessible throughout the plant and the capacity to turn that data into timely, economically significant action.
AI and robotic services are starting to make it happen. These technologies offer visibility into parts of the plant that were previously inaccessible, and connect that insight to faster decision-making, helping O&M teams and asset owners define a new standard for plant performance that is characterized by continuous awareness, proactive intervention, and better risk-adjusted results.
We have identified five ways that AI and robotic services are helping to set a new benchmark for operational efficiency and helping to cut the levelized cost of energy (LCOE) throughout the project lifetime.
1. Components Check Before Commissioning
Many performance gaps occur before the facility is even commissioned. The DC balance of system – connections, wiring, fuses — is a typical site of failure, as we all know. HelioVolta’s SolarGrade PV health assessment, which analyzes field inspections of hundreds of solar projects in development and operation, finds that wiring and connection problems are found in over 80 percent of projects tested. These hazards are omnipresent and frequently invisible.
These issues are even more difficult to identify at a gigawatt scale. Aerial inspections do not see components beneath the array. Manual inspections are similarly difficult to scale and to really do, since components are generally hidden.
Furthermore, a recent study of more than 2 GW of utility-scale inspections by Nextpower’s NX Ranger robot points to a shortcoming of standard thermal examination.
The data showed that 79% of high-risk connector and fuse issues – cracked housings, improper connections, insulation degradation, partial disconnections – had no thermal signature at the time of inspection.
This gap is filled by ground-based robots like the Ranger that use thermal and optical cameras to investigate underneath the array, collecting high-resolution, precisely geo-tagged data at the component level, offering visibility where conventional approaches fall short. This system is deployable at scale before commissioning for an end-to-end QA/QC audit.
2. Early and Autonomous Identification of Fire Danger
The solar sector has previously shown how better visibility and automation may lower risks in the face of harsh weather. Insurers are starting to see the benefits of the gains made in hail exposure via the development of weather forecasts and automatic tracker controls.
In 2025 alone, post-event customer surveys indicated Nextpower tracker systems performed over 2,000 hail stows globally with less than 0.007% reported module damage.
Fire is the next frontier for risk mitigation. According to Axis Capital, fire is the second highest loss cause for utility-scale solar projects in terms of gross claims in North America.
“Fire accounts for about 20% of losses, both in dollar amount and by count,” says kWh Analytics.
Additionally, a kWh Analytics study indicates that over 80% of solar fires begin on the property, with PV equipment as the major cause of ignition. In 3% of those situations, wiring or connections have been found to be the cause of the fire, but a further 27% remained ascribed to unknown sources, which suggests that underlying faults are not being spotted early enough.
Advances in imaging and AI are giving operators the ability to identify early signs such as smoke, temperature irregularities and even environmental variables such as plant growth that might lead to fire danger, regardless of their source.
Earlier detection of these situations allows operators to respond before problems become big occurrences, lowering operational and financial risk.
3. Accelerating the Move From Detection to Diagnosis
Conventional inspection procedures tend to isolate detection from diagnosis.
Aerial inspections are good at finding abnormalities, but such alarms usually need a second step – sending professionals out to the field for additional investigation. The delay might leave problems unsolved for long periods of time.
Robotic services break down this procedure. Combining routine imaging with AI-based analysis, they can not only detect when anything is wrong but also diagnose what is wrong and where.
Findings are well localized, contextualized, and turned into practical outputs, frequently including prioritized work orders and repair recommendations. This allows operators to go straight from detection to decision to action.
4. Economical Optimization of Panel Cleaning
There are performance losses that are not connected to discrete failures. Some are slow, varied, and not easily quantifiable, soiling being one example. According to IEA PVPS, soiling causes 4-7% of the worldwide energy loss.
Soiling builds up unevenly, and it reacts inconsistently to weather, depending on local circumstances such as dust, pollen, and agricultural activities. Soiling has been addressed indirectly for years via set cleaning schedules or reactive actions. That was okay when portfolios were smaller, and margins were more forgiving. It doesn’t work as well anymore.
Operators are left to interpret signals never meant to detect loss without direct measurement. Cleaning too early is a waste of O&M expenditures. Cleaning too late is a waste of energy.
Sensor-based approaches narrow this gap by directly measuring the effect of soiling under real operating conditions. Operators can quantify energy loss in real-time and make cleaning decisions based on actual conditions by comparing clean and soiled reference performance.
Cleaning is an economic choice, not a planned one. Does the value of the recovered energy surpass the cost of action? That analysis immediately leads to action, with real-time soiling data allowing for robotic cleaning devices to be deployed at the most opportune moment.
5. Data Integration in a Dynamic Digital Twin
And it’s not just efficiencies in O&M. This real-time data provides a new level of visibility, verification and assurance.
The next step-change will be to integrate these inspection, monitoring and performance data streams into a single digital twin – a living, high-fidelity replica of the entire power station. In this environment, everything from trackers, connections and autonomous robots is uniquely tracked as digital entities and represented in a 3D map-based representation of the complete solar site.
This intelligence layer transforms individual data points into a linked, self-aware power plant that provides all stakeholders, including owners and operators, with unparalleled insight into plant operations.
Raising the Standard
Solar is increasingly characterized by risk-adjusted LCOE, and the capacity to mitigate uncertainty is becoming a competitive advantage. With insight into previously unreachable portions of the plant, and converting that vision into action with verifiable outcomes, AI and robotic services are creating a new standard of how solar assets are monitored, validated, and optimized across their lifetime.
This transition minimizes uncertainty, enables better planning, and offers asset managers more assurance that their facilities are working as intended.
