Enhancing the energy efficiency in district heating networks through digital twins

Abstract

Europe prioritizes energy efficiency for sustainable development and climate action, with buildings consuming 40% of the EU’s energy and contributing over 30% of CO2 emissions. To address this, the EU implements policies targeting energy efficiency across sectors like industry, transportation, and district heating. The Energy Efficiency Directive mandates reductions in energy consumption, promoting measures such as energy audits and performance standards. Initiatives like the European Green Deal and Renovation Wave Strategy underscore the importance of energy efficiency in achieving carbon neutrality. District heating systems play a vital role in this strategy, offering Efficient heating solutions for urban areas, and optimizing their operation is a key focus of research. Techniques include mathematical modeling, advanced control strategies, and integration of renewable energy sources. This work focuses on the application of a digital twin to model the district heating network combined with prediction tools to enhance system efficiency. A procedure to integrate physics (white-box) and data-driven (black-box) approaches to foster better-informed decisions in the optimal energy resources management is presented.