AI in Renewable Energy
There are mainly two types of energy: renewable energy and non-renewable energy. Non-renewable energy includes fossil fuels like natural gas, petroleum and coal. However, these energy sources come from nature itself; it is impossible to renew them quickly.
In addition, fossil fuels emit greenhouse gases that are responsible for global warming.
According to the report published by the Global Carbon Project, carbon dioxide emissions reached an all-time high in 2018.
On the other hand, renewable energy includes energy sources that are available in infinite quantities, such as sunlight, air and water. These resources are renewable and release very few harmful gases.
In the last decade, many developed countries globally have shifted their focus to producing renewable energy. Governments are planning to be dependent on green energy. Still, the industry has its own set of challenges since we are dependent on getting energy from sources that are not in our control. Also, these resources are not available in the same amount in all different parts of the Earth.
The U.S. Energy Information Administration (EIA) reports that as of 2020, 102.9 million smart meters had been installed across the United States.
By some accounts, each smart meter produces 400 megabytes of data per year. Do the math, and that’s a staggering 41 petabytes of information from smart meters alone — and that’s only a fragment of the total volume of projected data utilities will generate from smart devices. As the grid is modernized with smart sensors and other smart grid technologies, those devices will also produce massive amounts of data. Big data indeed!
By leveraging the power of AI, power companies can get better forecasts, manage their grids and schedule maintenance.
Renewable energy is primarily dependent on resources like sunlight, airflow and water. All of these resources are tied up with the weather, which is something humans can’t control. Artificial intelligence has helped in overcoming this challenge because it is a reliable tool for forecasting the weather.
With the use of machine learning, it analyzes the current weather and historical weather data to provide accurate forecasting. The power companies use that forecast data to manage the energy systems. If there is a good forecast, the companies produce renewable energy and store it. If the forecast is terrible, power companies manage their load based on that. They plan for the problem and utilize the help of fossil fuels to keep the power supply uninterrupted.
These technologies use data analytics to predict energy consumption in households. The prediction is based on the specific part of a year and also considers previous years’ data.
This helps power companies stay informed about how much energy will be required in the upcoming days. Based on that, they can manage their grids without any outage. If the consumption is going to be high, they can ramp up energy production. Alternatively, in some parts of the year, when energy consumption is low, they can lower the production to avoid wastage.
No matter how well power grids are managed, there are times when they need maintenance. It is crucial to run the entire system efficiently. By leveraging the power of AI and machine learning, the specific part of the system that needs maintenance can be easily predicted.
When power companies are updated with upcoming maintenance work, they can notify consumers about maintenance in the grid. Scheduled maintenance means consumers can be aware of the forthcoming power cuts. What we witness currently is power cuts without any early announcements.