Self-learning weather model and renewable energy forecasting technology analyzes data from 1,600 weather stations, solar plants, wind farms, and weather satellites to generate weather forecasts and predict renewable energy availability up to weeks in advance. The system continuously refines its models as it receives new data, and its predictions can help regional power grids integrate renewable energy sources better, as changes in the weather can cause renewable energy production to vary greatly.
Machine learning tool creates highly granular models of a building’s energy efficiency with an error rate below one percent and recommends improvements. The simulations account for approximately 150 parameters that influence energy efficiency, such as lighting and ventilation, and learn how to optimize these factors to maximize a building’s energy efficiency.
Machine-learning system predicts variations in wind speeds over time to help power companies more quickly evaluate potential locations for wind farms. Traditionally, a power company will gather 12 months of wind-speed data to evaluate a potential wind-farm location, but the machine-learning system can produce more accurate models with just three months of data by correlating data from multiple sites and weather stations.