In recent years, the renewable energy industry has experienced some revolutionary changes thanks to advances in artificial intelligence (AI). By integrating AI with renewable generation systems, utilities can unlock substantial savings and boost system reliability, while also reducing carbon emissions and other environmental impacts. As AI technology continues to advance and generate even more value for the renewable energy sector, it will play an increasingly important role in the transition to an emission-free grid. Here’s how AI technologies are already helping renewables revolutionize the energy industry.

Energy Storage

Batteries are a cost-effective way to store excess solar and wind energy so it can be used at night. Without cheap, effective storage, renewables can’t compete with coal or natural gas. Storage not only helps make intermittent energy reliable—it also allows for smarter use of energy overall. For example, if your solar panels are creating more electricity than you need in late afternoon and evening, that electricity could be stored for use later—saving you money.

Predictive Analytics

One of the biggest roadblocks to renewable energy adoption is a lack of control—wind and solar generation can be unpredictable. That’s where big data and artificial intelligence comes in. Using predictive analytics, we can predict wind levels and weather patterns to produce more renewable energy when it’s needed. As renewables are increasingly used to meet our power needs, they will start to become a real alternative to traditional fossil fuels.

Data Collection

As more and more renewable energy generators connect to the grid, it becomes necessary to collect more data on their performance. A growing number of companies are already using artificial intelligence (AI) to monitor power sources and predict problems before they occur. While none of these advances will replace human engineers, they’re providing insights that can help reduce inefficiencies and costs.

Grid Management

One of today’s biggest problems in renewables is grid management. Cloudy days or windless periods can mean a shortage of electricity, while overcast skies and gusty winds can lead to too much energy production. In both cases, it’s difficult for operators to find ways to smooth out these peaks and valleys in demand.

Load Forecasting

In order to understand where (and when) power will be needed, it’s critical to first understand what supply looks like. The process of finding out exactly how much electricity a utility needs to generate at any given moment in time—in other words, forecasting loads—is called load forecasting. The sheer complexity and amount of data involved means that old-school methods can’t work alone: many utilities are partnering with machine learning firms who can process huge amounts of data, helping them forecast with precision.

Autonomous Vehicles and Self-driving Cars

Believe it or not, we’re almost at a point where our cars can drive themselves. Autonomous vehicles are already being tested in select cities around the globe, and are expected to hit mass production in 2020. Autonomous vehicles will be able to take humans from Point A to Point B faster than ever before – and with more energy efficiency.


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