Mimicking the Mind: How AI Will Change the Solar Industry

(Arbox Renewable Energy) – Though the concept of artificial intelligence (AI) has a wide variety of working definitions, it generally refers to machines that can learn from and act upon data sets without human programming or intervention. Moving from the general to its more constituent types, AI can be broken down into machine learningdeep learning and neural networks. Without getting too technical, essentially the whole premise of AI is a machine that mimicks the human brain. The machine can learn and adapt to different scenarios, and as times passes the machine gets smarter and reacts differently to achieve better results. AI will certainly play a pivotal role in many industries, notably through improved business intelligence software that solves problems quicker than humans. The application of all forms of AI to the solar industry is no exception.

AI in frequency regulation

In North America power is produced at frequency of 60 Hz AC. This means the direction of the current changes 60 times per second. With the growth of renewables coming onto the grid, keeping the frequency in its tolerable band (close to 60 Hz) has been challenging for grid operations. This is because renewables such as wind and solar are intermittent sources of power. Traditionally gas turbines and coal generation plants have been used to keep the frequency stable, but they are slow to respond. Now operators are looking at faster-response options, such as battery storage, which in a perfect world is able to maintain grid frequency and avoid outages. The growing interdependence of solar on storage has created new opportunities for AI.

Generally, a human has to look at power supply and demand and then adjust controls in order to keep the entire system in balance. But human decision-making cannot keep up with the rapidly changing levels of supply by renewable sources of energy (changing weather patterns, shifting seasons, and regional micro-climates); however, sophisticated AI systems are more than capable of learning data patterns from smart meters much faster than humans, and can better predict when additional power resources are needed immediately and in the long-term.

AI in weather forecasting

AI can also be useful in weather forecasting. Accurate weather forecasting helps utilities make smarter decisions about operations during severe weather conditions such as hail, thunderstorms and hurricanes. AI can analyze large volumes of historical and real-time data from satellites, weather stations, and IoT devices to recognize patterns and predict weather that could impact renewable energy production.  This information can allow power producers to adjust accordingly.

AI to optimize power plant performance

AI also can be used to maximize performance of power plants. For instance, if you have 10 power plants in your portfolio, maybe eight are performing at 90% and two at 75%. AI can analyze data—region, system, slopes, humidity, irradiance, manufacturer—to recognize anomalies or issues that a human may not. Furthermore, AI can be used for predictive maintenance by learning algorithms to spot inconsistencies and determine when a panel or an inverter is about to fail.

What’s coming next?

AI will increasingly automate operations over the next several years in the solar industry and boost efficiencies across the renewable energy sector. “We expect the installation of more sensors, the increase in easier-to-use machine learning tools, and the continuous expansion of data monitoring, processing and analytics capabilities to create new operating efficiencies—and new and disruptive business models,” said Lucy Craig, director technology and innovation at DNV GL – Energy. According to Craig, stakeholders in the solar industry will see benefits of artificial intelligence in several areas, including:

  • Robotics growing in prevalence for remote inspection, with new benefits in maintenance and troubleshooting. Crawling robots that can get close to a structure’s surface enabling a new set of technologies such as microwave and ultrasonic transmitters and receivers, which can be used to penetrate structures to reveal faults in materials.
  • Supply chain optimizations by autonomous driving robots, which can in future build entire onshore wind or solar farms: parts of a wind turbine or a solar array are transported from the factory by self-driving lorries, unloaded by another set of robots, attached to the foundations that yet other robots have dug and filled, and pieced together by a final set of robots and drones.
  • Autonomous drones with real-time artificial intelligence-supported analysis will become the primary tool for carrying out effective and efficient inspections of wind turbines and solar panels.

According to Craig, “Solar developers, operators, and investors would be wise to consider how their industry can use AI, what the impacts are on the industry in a larger sense as well as the specific decisions that individual managers need to confront.”


Farid Najafi is the president of Arbox Renewable Energy, which specializes in asset management software in the solar industry.

This article was originally published here at Solar Power World.

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