Why undetected degradation is costing solar operators and how AI can help

Solar photovoltaic systems, a key pillar of global decarbonization strategies, are generating record amounts of clean energy. However, many installations operate below their theoretical potential due to undetected degradation. Industry-standard monitoring tools struggle to separate long-term efficiency loss from normal weather-driven fluctuations, limiting early intervention.

A study published in the journal Applied Sciences addresses this limitation. Titled Deep Learning-Based Detection and Forecasting of Performance Losses in Solar PV Systems Using Multi-Sensor Data, a study published in Applied Sciences introduces a degradation-aware deep learning model for forecasting performance losses using multi-sensor operational data.

Why traditional solar monitoring falls short

Most solar PV monitoring systems are built around power output forecasting. These systems use historical production data, weather inputs, or physical models to estimate expected energy generation. When actual output deviates from predictions, the difference is often attributed to changes in irradiance, temperature, or short-term environmental variability.

According to the study, this approach masks a key problem. Performance losses caused by degradation processes are gradual and persistent, making them difficult to separate from normal variability using conventional forecasting models. As a result, degradation is often detected late, when losses are already substantial and maintenance becomes reactive rather than preventive.

The research highlights that degradation in solar PV systems is not a single phenomenon. It can stem from panel aging, micro-cracks, inverter inefficiencies, dust accumulation, thermal cycling, or humidity exposure. These processes unfold over long timescales and interact with environmental conditions in complex ways. Traditional models that focus on predicting absolute power output struggle to capture these dynamics.

The author notes that effective monitoring requires reframing the problem. Instead of asking how much power a system should produce at a given moment, operators need to know whether the system is performing as well as it should under those conditions. This shift moves the focus from output prediction to health assessment.

To operationalize this idea, the study introduces a performance loss ratio. This metric represents the deviation between observed power output and an idealized reference model that accounts for environmental conditions. By normalizing performance in this way, intrinsic losses tied to system health can be isolated from short-term weather effects.

This conceptual shift forms the foundation of the study's AI framework and distinguishes it from much of the existing literature on solar forecasting.

A degradation-aware deep learning framework

The study introduces a hybrid deep learning architecture that combines convolutional neural networks and long short-term memory networks. This design reflects the dual nature of the data and the problem being addressed.

Solar PV systems generate rich streams of multi-sensor data. These include solar irradiance, panel surface temperature, ambient temperature, humidity, wind speed, and inverter power output. Relationships among these variables are both instantaneous and temporal. For example, sudden temperature spikes can affect efficiency in the short term, while gradual thermal stress contributes to long-term degradation.

The CNN component of the model is used to extract local patterns and interactions across sensor channels. It captures how combinations of environmental and operational variables influence performance at a given time. The LSTM component then models temporal dependencies, allowing the system to learn how performance loss evolves over days, months, and seasons.

By training the model on performance loss ratios rather than raw power output, the framework learns to recognize signatures of degradation embedded within noisy operational data. This enables it not only to detect existing losses but also to forecast future degradation trends.

A major challenge in this domain is data availability. Large-scale labeled datasets that explicitly capture degradation events over long periods are rare. Solar installations vary widely in design, location, and maintenance practices, making standardized datasets difficult to assemble.

To address this, the study adopts a hybrid data strategy. Real operational data from a photovoltaic installation in Ankara are used to establish realistic statistical properties, seasonal cycles, and sensor correlations. These characteristics then inform the generation of a synthetic multi-sensor dataset that preserves physical consistency while allowing controlled experimentation.

This approach allows the model to be trained and evaluated under diverse scenarios without relying solely on limited real-world degradation records. The study argues that such hybrid data strategies are essential for advancing AI applications in renewable energy systems where long-term labeled data are scarce.

Forecasting losses before they become failures

The results reported in the study show that the degradation-aware CNN–LSTM model outperforms several commonly used baseline methods, including persistence models, linear regression, and gradient boosting approaches. Performance gains are observed across key error metrics, with particularly strong improvements in stability and bias reduction.

Importantly, the model demonstrates robust behavior under noisy sensor conditions. In real-world solar installations, sensor drift, missing data, and measurement errors are common. A monitoring system that performs well only under ideal conditions has limited operational value. The study shows that the proposed framework maintains predictive accuracy even when sensor inputs are imperfect.

The ability to forecast performance loss has significant practical implications. Rather than responding to unexpected drops in output, operators can identify emerging degradation patterns and schedule maintenance proactively. Cleaning schedules can be optimized to address soiling losses, component replacements can be planned before failures occur, and system performance can be benchmarked continuously against ideal behavior.

According to the study, even small efficiency losses matter at scale. A one or two percent decline in performance may appear negligible in the short term, but over the lifespan of a large solar plant, it translates into substantial energy and revenue losses. Early detection therefore has direct economic as well as environmental benefits.

The framework also supports fleet-level analysis. Operators managing multiple solar assets can compare performance loss trajectories across sites, identify outliers, and allocate resources more efficiently. This is particularly relevant as solar portfolios grow larger and more geographically distributed.

Future of solar operations

Explainability and interpretability, as the study stresses, are important. While deep learning models are often criticized for opacity, the use of performance loss ratios provides a clear conceptual link between model outputs and physical system behavior. Operators can interpret forecasts in terms of efficiency decline rather than abstract prediction errors, supporting trust and adoption.

There are, however, challenges ahead. Deploying such AI frameworks at scale requires integration with existing monitoring infrastructure, data governance practices, and maintenance workflows. Cybersecurity, data privacy, and workforce training also become important considerations as solar operations become more digitized.

Future research could extend the framework to incorporate additional data sources, such as infrared imaging or electrical signature analysis, and to adapt models dynamically as systems age.