Solar thermal technology has long been a staple in the renewable energy sector, converting solar radiation into usable heat for water heating, electricity generation and industrial heating processes. However, despite their promise, solar thermal systems are not without their challenges. Efficiency limitations, issues with system maintenance, and the intrinsic problem of solar intermittency all pose substantial hurdles.

Artificial intelligence (AI) is increasingly being looked at as a game-changer for solar thermal systems due to its ability to solve complex problems. AI solves these complex problems by trying to mimic human reasoning and using mathematics and logic to develop intelligent decision-making systems. The integration of AI into solar thermal technology promises a relationship that enhances operational efficiency, reliability and energy output.

The integration of AI into solar thermal technology promises a relationship that enhances operational efficiency, reliability, and energy output. Source: ryzhi/ Adobe StockThe integration of AI into solar thermal technology promises a relationship that enhances operational efficiency, reliability, and energy output. Source: ryzhi/ Adobe Stock

The role of AI in overcoming limitations in solar thermal systems

#1 Solar radiation prediction

Solar radiation data is a critical component in the design, operation and performance evaluation of solar thermal systems. Engineers rely on this data to understand the availability and variability of solar radiation in a specific location, which in turn helps with collector sizing, determination of ideal collector orientation, and prediction of energy output.

Traditionally, engineers rely on traditional empirical models based on meteorological data to predict solar radiation. These models typically used historical solar radiation records, temperature ranges and atmospheric conditions to forecast future radiation. However, the static nature and limited accuracy of these models are among their notable shortcomings.

With the advent of AI, the landscape of solar radiation prediction is undergoing a transformative change. Machine learning algorithms and neural networks are increasingly being used to create dynamic and adaptive prediction models that improve upon the traditional empirical models. For instance, AI algorithms can adapt to real-time data, continuously refining their prediction as new information becomes available. Moreover, high-precision machine learning models (such as those based on neural networks) can capture non-linear relationships and complex patterns in the data, resulting in a much higher prediction accuracy than traditional empirical models.

#2 AI-driven collector positioning

The performance of collectors for solar thermal systems is strongly influenced by the angle of incidence of the sun rays on the collector. Higher energy output can be achieved when the angle of incidence is very small (or very close to 0°). As a result, engineers design these collectors with solar tracking technologies that orient the collectors to minimize incidence angle.

One of the earliest tracking technologies featured pre-programmed systems where the angle of the collector is set at intervals. This technology typically follows a predetermined path based on typical sun movements but doesn’t adapt to real-time conditions. Another commonly used solar tracking technology is the sensor-based system that uses light sensors to detect the source of light and orient the collectors accordingly. However, these two methods are sometimes prone to errors.

The application of machine learning algorithms in these sensor-based systems can help optimize the angles of collectors in real time using data collected from sensors. Moreover, with the use of historical weather data, time of the year and other environmental variables, these algorithms can predict the optimal orientation of the collector, especially on days when the weather conditions may not be ideal.

Learn more about solar tracking devices on GlobalSpec.

#3 Smart maintenance scheduling

AI can also be crucial in optimizing the timing, scope and sequence of maintenance activities in solar thermal systems. For instance, one of the major maintenance activities is the cleaning of the collectors. With the use of on-field data, local weather data and historical fouling rates, machine learning algorithms can predict the best time to perform these maintenance activities to minimize disruption and maximize this system’s performance.

Learn more about maintenance planning and scheduling.


Integrating AI into solar thermal systems can help elevate the technology’s efficiency, reliability and output to new heights. By employing advanced algorithms for solar radiation prediction, collector positioning and smart maintenance scheduling, AI can address some of the inherent challenges of solar thermal systems.

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