As the global energy transition accelerates, grid flexibility has emerged as a critical enabler for decarbonizing electricity systems worldwide. However, unlocking this grid flexibility requires accurate, high-resolution forecasting of future global grid conditions. To meet this challenge, Electricity Maps has developed a robust, interconnected machine-learning platform that can predict the future state of electricity networks worldwide. Our approach leverages thousands of specialized ML models—each trained on granular data from a specific grid—which are then interconnected to account for the real-world linkages between cross-border electricity networks.
Íngrid Munné Collado is the Technical Lead and a Senior Machine Learning Engineer in the Grid Forecasts team at Electricity Maps, driving the development of scalable forecasting systems for power and carbon data. With a Ph.D. in Electrical Engineering and experience in electricity markets, demand response, and renewable energy forecasting, she contributes to developing forecasting models that enhance flexibility and accelerate the decarbonization of the electrical grid.
