Many people don’t think that solar energy is a reputable and cost-effective energy source. Instead, solar energy is often seen as unpredictable and expensive. After all, it’s not always sunny and building a solar array can cost a pretty penny.

Yet, solar installations have grown dramatically. In the last five years, the US solar power industry has grown by over 20% annually.

But concerns persist about the reliability and cost of solar. These concerns can deter companies and individuals from installing solar arrays. And while there have been favorable policies supporting solar, such as the Inflation Reduction Act, improving solar will only help continue this recent trend of increasing solar installations and favorable policy.

A solar array. Image credit: rawpixel

A recent study explains how artificial intelligence (AI) can improve the productivity of solar, making it even more reliable and less expensive. This new research says AI can make solar energy an even more useful source of electricity through better data analysis. 

To design a solar array, engineers examine huge sets of historical data, including solar radiation levels, geographic information, and weather patterns. That’s a lot of data that must be accurately analyzed for solar arrays to produce maximum energy. 

AI is able to optimize this data analysis. AI algorithms are sophisticated, so they are able to process more data, be more precise in their findings, and cut down on processing time. In doing so, more time is freed up for solar engineers to design better performing solar. AI can then be used in testing to predict how various designs would operate in order to decide on the best one. 

Much of solar design revolves around the basic equation driving solar electricity generation: more sun = more energy. AI’s superior data analysis capabilities can be used to help decide where to place solar panels. This is especially useful when looking to maximize the productivity of large solar facilities. 

AI can also improve solar panel tracking systems that track the sun’s location and shift panels to track the sun through the day. AI optimizes these systems by more quickly and continuously analyzing real-time temperature, solar radiation, and panel performance data to calculate adjustments for individual panels. Industry statistics say AI could improve solar panel productivity by 20%.

This real-time data analysis also allows AI to improve monitoring of existing solar arrays. It can track current, voltage, and temperature from panels to forecast maintenance needs. AI systems are good at recognizing patterns. That means AI can learn from past arrays’ maintenance records to determine likely outcomes from the real-time data it’s receiving.

These AI driven strategies have been successful in other fields. For example, Google noticed that a lot of its energy usage went to cooling its data centers. To optimize their cooling systems, Google first trained an AI model with historical data from the data center. 

Drawing on that machine learning, the AI system was then able to monitor real-time data from the center and make adjustments to temperature setpoints and fan speeds. The result? The AI system was able to reduce the amount of energy used for cooling by 40%. If those gains could be replicated for solar electricity generation, it would be a substantial improvement.

AI researchers also anticipate AI can improve the performance of solar panels with energy storage. Energy storage allows solar energy to be used at a later time, such as at night or when a cloud passes over. 

AI’s computationally efficient real-time processing of solar generation projection, energy consumption, and electricity pricing data allows it to optimize energy usage by deciding when electricity goes to the grid – for real-time consumption – or to batteries – for later use. That storage makes renewable energy available at night or during inclement weather. Everyone likes leftovers, right?

Overall, AI can improve the accuracy of solar forecasts by up to 30%– a figure that is expected to grow over time. 

While this is great news, it is important to think about potential complexities as well. For example, AI models are expensive to create and sustain. Many people and companies will not have the financial means to implement AI in their solar power productions.

Nevertheless, AI solar applications will become more reliable in the near future as technology continues to advance. As long as there is careful consideration of potential complexities, AI is sure to improve the field of solar energy and diminish people’s doubts of the industry.