Research

Computational engineering to tackle global challenges.

Cimne menu projects

Discover our latest research projects

Innovation

Delivering tangible solutions for the benefit of all.

Cimne menu nuclear

See how advanced simulation enhances nuclear safety

Community

A thriving network of global innovators, thinkers, and life-long learners in numerical methods.

Cimne menu unesco

Learn how the UNESCO Chair in Numerical Methods spearheads frontier innovation in the Global South

About

We are a pioneering research and innovation centre in computational engineering, founded in 1987.

Cimne menu people

People at CIMNE: Meet the talent that makes it possible.

See how advanced simulation enhances nuclear safety

Learn how the UNESCO Chair in Numerical Methods spearheads frontier innovation in the Global South

News

Back

ESEficiencia dedicates an article to EN-TRACK project

Oct 17, 2022

The specialized blog in energy efficiency, ESEficiencia, has recently published an article about EN-TRACK, an EU-funded project coordinated by CIMNE in consortium with the Government of Catalonia (Department of Territory and Sustainability) and the Catalan Institute of Energy, among other partners. The project builds on an existing infrastructure and will enable massive data collection, making the data comparable and interoperable with other existing databases and data analytics. The lack of statistical data on the real energy and cost savings achieved with them is one of the great challenges in terms of energy efficiency, a problem that this project addresses head-on.

EN-TRACK

The article of ESEficiencia explains the consortium, the methodology of the project, the stakeholders, the results and the pilot projects. According the article, the forecasts are that EN-TRACK will contribute significantly to reaching the climate objectives of the European Union for 2030, since the building stock represents 40% of CO2 emissions.

Related News

CIMNE-ETSII Lab Uses AI and Satellite Data to Monitor Water Quality
CIMNE-ETSII Lab Uses AI and Satellite Data to Monitor Water Quality

Experts at the CIMNE-UPM ETSII Lab (Aula CIMNE-UPM ETSII) have evaluated the effectiveness of Machine Learning (ML) and satellite imagery to assess water quality in inland settings. In a recent study, Ms. Laura Cáceres, Dr Jorge Rodríguez Chueca and Dr David J....

Tags

Share: