People use a forecasting model to predict how much electricity they will need in the next five, 10, or 20 years. They organize the predictions into different levels, like types of customers (like homes, businesses, or industries) and details about the electricity distribution system. This distribution system can have lots of parts, so the forecasting method needs to handle a large scale.
Experts, called load forecasters, are good at making these predictions. But now, there is a new challenge. More and more, we are using different sources of energy, like solar power, wind power, electric cars, and better batteries. This makes the predictions more complicated.
A report from the U.S. Energy Information Administration says we’ll keep using more renewable energy. To predict how much power, we get from things like solar farms, we use advanced computer programs called deep learning algorithms. Accurate predictions help us see the value of using renewable energy. When we use more renewable energy, we do not need as much from traditional sources, like fossil fuels.
There’s something called a “duck curve” that shows how our electricity use changes during the day. The curve gets lower in the middle of the day when we get a lot of solar power. But, the challenge is when the curve goes up quickly, like in the afternoon when solar power is not as strong. At that time, we need traditional power sources to be ready to make up for the drop in solar power.
The power system has to keep a perfect balance between how much electricity is made and how much we need. If it’s not balanced, we might have power outages or problems with the quality of power, which can harm equipment. Using analytics, or smart computer analysis, can help make sure everything stays safe, reliable, and costs less for both the power company and the customers.
