True decarbonization requires more than just ambition; it demands a holistic approach to understanding the complex energy flows of an economy. It is essential to not only pinpoint energy demands across different sectors but also to identify exactly which technologies—and in what proportions—can meet those needs sustainably and profitably. This challenge is the driving force behind MERLIN, a multisectoral energy planning tool built on the foundation of Machine Learning and Geographic Information Systems (GIS).
Born from the evolution of the Solar Thermal Systems team at Fraunhofer Chile, MERLIN was designed to create optimized decarbonization scenarios. By integrating data from the industrial, residential, transport, public, and commercial sectors, the software facilitates precise planning tailored to the specific realities of each locality. The system feeds on a comprehensive, up-to-date database—gathered from public records, interviews, and fieldwork—which is processed using machine learning to generate detailed, dynamic maps of energy supply and demand.
Beyond mapping, MERLIN applies advanced optimization models to determine the ideal locations for energy infrastructure. It rigorously evaluates costs, emissions, and technical feasibility across a range of renewable technologies, including solar energy, heat pumps, hydrogen, and grid connections. To ensure these insights are actionable, the project features an advanced user interface that allows stakeholders to interact directly with the maps and simulations.
Currently advancing toward validation in real-world conditions (TRL 7) with a robust plan for international commercialization, MERLIN is a collaborative effort led by Fraunhofer Chile and Greenventory, a high-tech spin-off from Fraunhofer ISE in Germany. Operating within the framework of the Eureka network's Eurogia cluster, the initiative is jointly funded by Corfo in Chile and the Central Innovation Program for SMEs (ZIM) in Germany.