Machine Power

Importance and application of AI in the solar power segment

The power sector is not new to technology disruptions, whether it is a shift to cleaner technologies or the use of digitalised operations to improve efficiencies. The latter has made significant strides in the Indian solar power space. In this segment, the use of artificial intelligence (AI) is gaining traction, with immense scope across various applications.

Importance of AI

In simplified terms, AI is intelligence demonstrated by machines. It is based on the simple principle of making a machine mimic the human brain. It is actively being used in renewable energy projects across countries. Software and computer applications have become so advanced today that a machine can be programmed to learn, adapt and react to the various performance parameters and operational processes of a solar power plant. Going a step further, these technologies can not only identify faults but they can self-learn to predict such breakdowns before they occur. This is leading to improved plant performance, as the ability to diagnose faults and breakdowns and thereby rectify them has improved significantly, thus reducing the time taken for due diligence. With all these gains in operational efficiencies, the time taken for rigorous planning and analysis has also been reduced.

Applications of AI

AI has multiple applications in the clean energy space. Due to the intermittent nature of solar energy, maintaining the frequency of a solar power plant at the accepted grid range is a cumbersome task. To this end, the use of AI in energy storage is being explored to maintain grid frequency and avoid outages. The technology can achieve this by helping predict the weather pattern through the analysis of large real-time weather data sets from multiple sources such as satellites, weather stations and other devices, and comparing them with historical weather data. AI can also predict how the weather would impact solar production, thus allowing power producers and operators to adjust and schedule power accordingly. In addition, there is a strong business case for the automation of services and the use of AI in the solar operations and maintenance (O&M) space, for two key reasons. One, manpower costs are reduced with automation. The low tariffs being discovered in solar auctions have squeezed the profit margins of O&M players. With the automation of services, these players can potentially maintain profitability. Two, O&M players can use technology to monitor the condition of components and assess plant performance in order to avoid penalties.

Another tool that can be created through AI is the “digital twin”, which is a virtual replica of an actual physical asset. This can be used as a benchmark for identifying faults through data anomalies. This is helpful in O&M, as operators can be alerted in this manner about possible breakdowns, which can then be rectified beforehand to improve plant performance. For predictive maintenance in particular, technologies have matured from computer-based automated monitoring systems to drones, robots and wearables, and now to solutions based on AI. Innovative AI-driven developments are also taking place in the module cleaning space. AI can be used to devise optimal cleaning solutions, taking into account the inclination of solar panels, geographic location, wind direction and speed.

Machine learning

AI is facilitating the development of another crucial technology – machine learning (ML). This is the study of computer algorithms that improve automatically through experience and the use of data. This field has an added advantage over AI. With advanced AI techniques, equipment can be programmed to learn and react to the various operational processes and issues of a solar power plant. With ML programs, however, these machines can be made smarter through self-learning, thereby enhancing the diagnosis and rectification of faults. To improve plant performance through advanced analytics, various programs are being developed in the ML space. Going forward, self-monitoring and self-operating machines will learn and evolve to become self-reliant to such an extent that they can take care of any present and future problems arising in renewable energy plants.


The supply chain disruptions that followed the Covid-19 pandemic have made the case stronger for the use of AI and ML in the solar power sector. Greater dependence on automation will reduce the requirement for manpower to physically visit a plant site for regular maintenance checks. With the falling costs of a wide range of AI tools and machines, the use of such technologies will become a norm and not an exception in the Indian solar power segment.

By Sarthak Takyar


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