Machine Learning & Solar Technology
Machine learning can be intimidating. While the process is extremely complex, it becomes far easier to understand when you realize it’s everywhere. Machine learning is a method of data analysis that automates analytical model building. Simply put, machine learning is a computer science that uses statistical techniques to give systems the ability to “learn” without being explicitly programmed.
You may not realize all the machine learning that is around you. Self-driving cars must continuously learn and evolve to be safe and successful. Ever noticed your recommendations on what shows and movies to watch next on Netflix or Hulu? That’s machine learning. Have you ever gotten an alert that someone is trying to buy gas in a state you’ve never been to with your credit card? Fraud detection programs use location, buying decisions, and other data to determine if a payment is authentic or fraudulent.
Industries all over the world are relying on machine learning to improve technology and business models. The solar industry is no exception. Here are a few examples of the tremendous impact that machine learning will have on the solar industry.
Lowering Solar Costs with Machine Learning
Like many other industries, the solar industry is rapidly embracing ways to analyze and crunch data to lower the costs of solar energy and open new markets for their technology. By connecting millions of data points, multiple aspects of the solar process can be made more efficient and more cost effective.
Smaller startup companies are using data to solve various problems across the solar industry while bigger companies are spending money on tracking, monitoring, and evaluating data from solar projects worldwide. Data received from solar panels can also be used to evaluate the maintenance of solar arrays and lower production costs.
Machine learning could help make solar last longer. A British startup, Azuri, which sells solar panels and batteries that are managed with cell phone technology, is using machine learning to study its customer’s usage and patterns to manage the batteries and power sources in an optimal way. For example, if a customer’s battery starts to get low, the system automatically adjusts the brightness of the lights and slows the rate of cell phone charging to make the energy last as long as possible.
Solar Forecasting Through Machine Learning
Machine learning is anticipated to make a large impact on solar forecasting. Many believe solar energy is unreliable and are skeptical about leaving the power of their homes up to a system that does not seem that dependable.
The ability to forecast solar conditions with accuracy is still a work in progress, but once it becomes a more idealized version it will be less difficult for utilities and other system operators to accurately gauge the output and demand.
IBM created a new system called SMT, a self-learning weather model and renewable forecasting technology, which pulls together different types of forecasting methods to provide a better overall picture. It uses deep machine learning techniques to blend domain data, information from sensor networks and local weather stations, cloud motion physics derived from sky cameras and satellite observations, and multiple weather predictions. The machine is trained on data from 1,600 sites around the U.S., making it 50 percent more accurate than the next best model. IBM is already ahead of the curve when it comes to solar and wind forecasting. They can currently predict solar conditions between 15 minutes and 30 days in advance.
Artificial Intelligence and Solar Technology
Artificial intelligence has been portrayed in movies, televisions, and pop culture for years now. One of the most recent examples is the 2018 Super Bowl commercial when Amazon’s Alexa loses her voice and celebrities step in to fill the role. Innovators, inventors, and tech companies have been focusing on artificial intelligence for decades and the focus is only gaining momentum.
There are many ways artificial intelligence will apply to solar technology:
- Advanced, autonomous robotics could conduct remote inspection and maintenance of solar farms communicating with on-the-ground units equipped to reveal specific faults.
- AI could be used to conduct due diligence procedures, reducing the time it takes to consider and analyze planning and investment decisions.
- Offers as a resource for forecasting, control and predictive maintenance.
Get Started With Solar
While the benefits of machine learning have not been fully realized in the solar industry, you can start reaping the rewards of solar energy.Learn more about residential solar