By Somesh Utkar, Omdena
Solar energy systems are designed to generate power consistently, but performance can decline due to factors such as soiling, shading, equipment degradation, and inverter issues. These losses do not always trigger immediate alerts and can go unnoticed, gradually reducing output.
Operators rely on monitoring systems to track generation and flag major deviations. While these systems provide visibility, they are designed to catch larger failures, not smaller performance losses that develop gradually and often go unreported across systems.
AI-based performance analysis addresses this gap by analyzing inverter and sensor data against expected system output. This enables earlier detection of deviations and provides a clearer view of how systems are performing under real operating conditions.
The insights in this article draw on Omdena’s applied AI projects in solar performance analysis and monitoring, highlighting practical challenges such as data inconsistencies, delayed fault detection, and the gap between expected and actual performance. These issues become clearer when examining how monitoring systems operate in practice.
Where Traditional Monitoring Breaks Down
In most solar plants, monitoring systems track generation and issue alerts when output falls below predefined thresholds. This approach works well for identifying major failures, such as inverter outages or complete system downtime, but is less effective at capturing smaller performance losses across systems.
Many real-world issues do not appear as sharp drops. Soiling, partial shading, module degradation, and sensor inconsistencies often cause gradual losses that remain within expected variability, making them difficult to detect through standard alert mechanisms. As a result, smaller but persistent performance losses often go unnoticed.
Operationally, teams rely on periodic reports and manual checks to identify such issues, which introduces delays and allows losses to accumulate over time. In many cases, these issues are identified only after reviewing historical data rather than through real-time alerts. This delay makes it harder to take timely, targeted action, as monitoring systems often show that output is lower than expected without clearly explaining why.
AI helps address this gap by analyzing large volumes of performance data across systems, making it easier to detect patterns that traditional monitoring approaches often miss.
How AI Tracks Performance Against Expected Output
AI-based performance analysis begins by estimating what a solar system should generate under given conditions. This expected output is derived from historical performance data along with inputs such as irradiance, temperature, and system configuration, creating a baseline for each system.
In applied projects, building this baseline requires working with real-world data from inverters and sensors, which is often inconsistent or incomplete. It also involves cleaning, structuring, and validating large volumes of data before it can be used effectively. Once defined, actual generation is continuously compared against expected performance.
Instead of relying on fixed thresholds, deviations are evaluated in context, taking into account changes in weather and operating conditions. This approach allows smaller performance gaps to be detected earlier and more consistently across systems. By tracking these deviations over time, operators can focus on systems that show persistent differences and prioritize where further investigation is needed. These deviations form the basis for identifying underperformance across systems.
Identifying Underperformance Across Systems
After deviations between actual and expected performance are identified, the next step is to determine whether they indicate real underperformance. Not every deviation requires action, as short-term variations can arise from weather changes, sensor noise, or temporary operating conditions.
Underperformance becomes relevant when deviations are consistent over time or observed across similar systems. For example, a system showing lower output than expected under similar irradiance conditions may indicate issues such as soiling, shading, or equipment degradation. Similar patterns often appear across multiple systems, making it necessary to compare performance across sites rather than relying on a single system view.
This stage focuses on patterns rather than isolated changes. By identifying persistent deviations and comparing performance across systems, operators can distinguish between normal variability and issues that require attention, making it easier to prioritize corrective actions. This process can also be supported by AI in solar energy, helping identify consistent deviation patterns while filtering out short-term variability.
Investigating and Validating Faults
With underperformance identified, the next step is to investigate the actual cause. This is not always straightforward, as similar performance drops can result from different issues such as soiling, shading, equipment faults, or data inconsistencies.
A key challenge is distinguishing real faults from false signals. Sensor errors, missing data, or gaps in data collection can create deviations that appear as faults but do not require action. This makes it necessary to validate signals before taking corrective steps. Likely fault patterns can be identified by correlating performance signals across different data sources, reducing the effort required to confirm whether an issue is real.
This process involves combining performance data with additional context, such as inverter-level metrics, historical trends, and site-specific conditions. In many cases, it requires filtering noisy data and validating signals across multiple time periods. By cross-checking these signals and confirming patterns over time, operators can narrow down the root cause and avoid unnecessary maintenance actions. These validated insights form the basis for making informed maintenance decisions.
Turning Insights into Maintenance Actions
Building on these validated insights, the next step is to turn them into clear maintenance actions. This involves deciding what needs to be addressed, how urgent it is, and whether it requires immediate intervention or scheduled maintenance.
Not all issues require the same response. Minor soiling may be addressed through routine cleaning schedules, whereas persistent inverter-related deviations may require targeted inspection or repair. Prioritizing actions based on severity and impact allows teams to allocate resources more effectively.
Issue prioritization can also be supported by analyzing severity and historical patterns, helping teams focus on the most impactful actions. By linking performance analysis directly to maintenance decisions, operators can move from reactive troubleshooting to more structured and proactive workflows. This improves response time, reduces unnecessary site visits, and helps teams focus on systems showing consistent performance issues.