AI-enabled analysis of meteorological data helps renewable energy producers match supply and demand
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Spotted: Weather forecasting becomes doubly useful when applied to renewable energy sources. Sweden’s Greenlytics company combines expert meteorology and data analytics with machine learning to predict how much power is likely to be produced and how much is likely to be needed by consumers. By creating a big picture understanding of how current weather conditions fit in with typical patterns for each region, the system maps production data across expected consumption.
WindMind, SolarMind, and LoadMind systems help renewable energy producers and distributers more accurately match production to variations in the volume of power used by a community. The systems combine satellite data with ground measurements that include air pressure, temperature and wind speed. As the AI learns how local topography affects weather conditions and energy output—as well as how community use varies across time—the systems’ use predictions become more accurate. Energy system operators have the option to add live production data to the system for even more accurate short-term predictions.
All three systems are provided as a cloud service and are designed for ease of use at any scale, from personal homeowners with a small array of panels, to energy farm managers overseeing thousands of devices across multiple sites. Greenlytics provides free demonstrations.
Renewable energy is increasingly being used to reduce waste and provide power from underused sources. Springwise recently spotted a flexible generator that wraps around pipes in order to turn waste heat into electricity, and a solar-powered cement production process.
Written by: Keely Khoury