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.
Marcus Garsdal is a MLOps Engineer within the Grid Forecasts team of Electricity Maps, orchestrating the lifecycle of the thousands of machine Learning models used by a forecasting engine that predicts the future state of electricity grids worldwide. Relying on his previous experience working at a Danish energy and weather forecasting company, delivering operational forecasts to +100GW of renewable capacity, as well as his academic knowledge of sustainable energy topics and machine learning, Marcus is a driving force for developing the grid and carbon data necessary to unlock the large-scale flexibility required for ensuring a greener future.
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.
