In today’s eco-conscious culture, “solar energy” is a buzzword phrase no longer merely embraced by self-professed tree huggers. Businesses and individuals alike are turning toward solar energy and away from fossil fuels, but the associated costs are still too high for some. However, forward-thinking analysts and companies feel confident about methods of making the costs more reasonable. Specifically, they’re investing in machine learning and big data.

Machine Learning and Big Data: Basic Definitions

Machine learning is a type of artificial intelligence involving computer-based algorithms that teach themselves through exposure to new data. That artificial intelligence reduces the amount of human input needed via programming. For instance, machine learning generates personalized Amazon product recommendations, and Google’s self-driving cars use the technology, too.

As the name suggests, big data involves gigantic sets of unstructured and structured data too large to examine with traditional data processing applications. Many businesses scrutinize this data to learn more about developing trends, customer associations and more.

Let’s look at a couple of examples of how experts use machine learning and big data in the solar industry. Before long, machine learning and big data may change how we view solar technology and make it more accessible to a larger market.

IBM Uses Big Data and Machine Learning to Predict the Weather

To reduce fossil fuel dependency, mega companies like Facebook and Google are buying renewable energy companies. However, since these facilities rely on energy from the sun and wind, accurately predicting the weather is essential for the largest return on investment. Computers must be able to withstand a myriad of environmental conditions, especially when used inside power stations. Potential environmental factors include dirt, moisture and vibrations, to name a few.

Recently, IBM teamed up with the Department of Energy and depended on big data and machine learning to improve weather prediction algorithms for solar and wind energy. The compiled data came from continual analysis of several large weather models, and representatives say the results are up to 30 percent better than conventional approaches. During the coming year, both organizations will partner with numerous power providers and examine how these new findings might make solar energy more feasible in certain parts of the United States, including New England.

Germans Use Tech to Predict Solar Power Needs

One problem with solar energy is that production stops when the sun goes down. Also, if a community installs too many solar panels, it might produce too much energy on a sunny day. To compensate, many companies use fossil fuels for baseline power and adjust output up or down depending on consumption needs. But, the adjustments are time consuming and costly to taxpayers since utility companies receive government compensation during lower output periods.

However, while working on a project called EWeLiNE, German researchers are using real-time data and machine learning algorithms to predict the total output of solar panels and wind turbines around the country across a 48-hour period. While studying data compiled over time, scientists compare real data with predicted amounts and adjust numbers to improve accuracy.

Scientists began the EWeLiNE project in 2012 and have recently launched a demonstration platform that allows transmission system operators to use live versions of the prediction software in their control centers. Normally, these experts are aware of power consumption levels, but they can only estimate how much power solar and wind farms will give to the grid. Thanks to the insight given by the EWeLiNE project, this may soon change.

There are 1.9 million solar and wind farms in Germany, but most can’t provide the necessary real-time data for the project, either because of data privacy laws or a lack of technology. Scientists are aware of those limitations and are working hard to minimize them over the coming years.

As these methods involving big data and machine learning, as well as their worthiness, in the solar industry illustrate, there are intriguing ways to use this technology to make solar energy more affordable and reliable. It will be interesting to see how things develop from here.