For nearly the past two centuries, humans have relied on coal, oil and other fossil fuels to cater to their energy needs, to power everything from their homes to cars to factories. This has led to unprecedented levels of emission of carbon dioxide and other greenhouse gases in the environment. The resultant climatic crises, therefore, are hardly surprising.

Renewable energy sources like wind, solar, biomass, geothermal and hydropower are being hailed as the harbingers of a new era in energy generation. Free of the planet-warming effects of fossil fuels, these alternative sources need to be brought to the mainstream to tackle global warming with any visible results. The implementation of novel AI-based approaches will add additional performance improvement of renewable energy systems – here's how:

• Weather forecasting: Weather is a highly unpredictable phenomenon. Its intermittent nature poses a significant challenge to solar and wind energy, the leading sources of clean energy. AI offers the potential to make better, quicker and more practical predictions than any traditional methods of solar forecasting or wind speed forecasting. 
• Enhanced plant efficiency: AI can play an important role in monitoring the performance of renewable energy plants. Further, with the use of digital twins in power generation equipment, it has become possible to gather patterns and insights in real time. Data-driven intervention enables accurate forecasts, helping to inform power supply and demand. This can increase productivity by making real-time adjustments, such as changing the pitch of the blades of a wind turbine.
• Grid management: The global transition to renewable energy will need AI technology to manage the distributed energy resources (DERs), such as microgrids, wind farms, private solar panels, and batteries. Millions of individual devices uploading and downloading electricity could create chaos for electric grids, invoking the need to balance supply with demand. The ability of AI to enable forecasting and control within seconds can result in a more resilient and flexible grid. 
• Predictive maintenance: The use of sensors in clean energy equipment offers rich datasets that algorithms can leverage to predict issues that might arise. In addition to establishing timeframes for regularly needed maintenance, AI-based diagnostics and optimisation solutions can save costs by predicting industrial-scale problems before they occur, such as a wind turbine failure. 
• Better farm design: The designing of clean energy farms on a commercial scale is a long and tedious process, often involving an army of various engineers over a period of several months. There are minute details that require attention and expertise, such as fixing the orientation and tilt of each solar panel based on the sun’s direction and wind speeds. AI algorithms are already being used to assist with all of these granular tasks within a matter of a few minutes.

Image by Cornell Frühauf from Pixabay