Energy Efficiency
Energy saving
RESEARCH AREAS
Energy Efficiency
Description
We offer our knowledge and ITC tools to support the design and maintenance of new buildings and energy efficient retrofitting projects, nearly zero-energy buildings and Positive Energy Districts (PEDs). To this aim we integrate innovative technologies to model, characterize and propose advanced and holistic solutions that combine the most promising technologies (passive and active) available in the market.
Our ICT developments allow us to manage buildings more efficiently, through the modelling and digitalization of their information, and implement advanced control strategies to optimize the use of energy while improving the indoor comfort conditions.
We energy audit existing buildings, analyse the energy consumption and propose measures to improve their energy efficiency and indoor comfort conditions.
Research lines
- Research in advance and intelligent strategies for the management, operation and maintenance of buildings based in AI/ML/DL for the generation of decision support systems.
- Application of digital enabling technologies for the improvement of buildings sustainability and intelligence.
- Building digitalization and generation of digital twins.
- Application of blockchain technology in the energy area.
Networks and Platforms
- A.SPIRE: A.SPIRE.
- ECTP: European Construction Technology Platform.
- EFFRA: European Factories of the Future Research Association.
- ISES: International Solar Energy Society.
- MANUFUTURE: MANUFUTURE.
Publications
- García-Fuentes, M.Á.; Álvarez, S.; Serna, V.; Pousse, M.; Meiss, A. “Integration of Prioritisation Criteria in the Design of Energy Efficient Retrofitting Projects at District Scale: A Case Study” Sustainability 2019, 11, 3861. DOI: 10.3390/su11143861.
- García-Fuentes, M.Á.; Serna, V.; Hernández, G.; Meiss, A. An Evaluation Framework to Support Optimisation of Scenarios for Energy Efficient Retrofitting of Buildings at the District Level. Appl. Sci. 2019, 9, 2448. DOI: 10.3390/app9122448.
- García-Fuentes M.Á., Hernández G., Serna V., Martín S., Álvarez S., Lilis G.N., Giannakis G., Katsigarakis K., Mabe L., Oregi X., Manjarres D., El Ridouane H., De Tommasi L.,”OptEEmAL: Decision-support tool for the design of energy retrofitting projects at district level”, IOP Conference Series: Earth and Environmental Science, Central Europe towards Sustainable Building (CESB19), Prague, Czech Republic, Volume 290 012129, July 2-4, 2019. DOI: 10.1088/1755-1315/290/1/012129.
- Martín S., Serna V.I., Álvarez S., García M.Á., Hernández G., Sicilia A., Costa G., “OptEEmAL: IT-Supported design tool for the generation of optimised energy retrofitting scenarios at district level”, 2019 European Conference on Computing in Construction (EC3 2019), Chania, Crete, Greece, July 10-12, 2019, pp. 246 – 255. DOI: 10.35490/EC3.2019.169.
- Sanz, R. & Álvarez-Díaz, Sonia & Valmaseda, Cesar & Rovas, Dimitrios. (2018). Automatic development of Building Automation Control Network (BACN) using IFC4-based BIM models. DOI: 10.1201/9780429506215-28.
- Hernández, J.L., Martín Lerones, P.; Bonsma, P., van Delft, A., Deighton, R., Braun, J.D. (2018). “An IFC Interoperability Framework for Self-Inspection Process in Buildings”. Buildings, 8, 32. DOI: 10.3390/buildings8020032.
- Hernández, J.L., Sanz, R., Corredera, Á., Palomar, R., Lacave, I. (2018). “A Fuzzy-Based Building Energy Management System for Energy Efficiency”. Buildings, 8(2), 14. DOI: 10.3390/buildings8020014.
- Corredera, Alvaro & Macía, Andrés & Sanz, Roberto & Hernandez, Jose. (2016). An automated monitoring system for surveillance and KPI calculation. 1-6. DOI: 10.1109/EESMS.2016.7504806.
- S. Martin, J. Hernandez and C. Valmaseda, “A novel middleware for smart grid data exchange towards the energy efficiency in buildings,” 2015 International Conference and Workshops on Networked Systems (NetSys), Cottbus, 2015, pp. 1-8. DOI: 10.1109/NetSys.2015.7089063.
- Sanz-Jimeno, R., Álvarez-Díaz, S. A tool based on the industry foundation classes standard for dynamic data collection and automatic generation of building automation control networks. Journal of Building Engineering, vol. 78, p. 107625, Nov. 2023. https://doi.org/10.1016/j.jobe.2023.107625
- Mulero-Palencia S, Álvarez-Díaz S, Andrés-Chicote M. Machine Learning for the Improvement of Deep Renovation Building Projects Using As-Built BIM Models. Sustainability. 2021; 13(12):6576. https://doi.org/10.3390/su13126576
Reference clients:
Team
Ali Vasallo Belver
Head of Energy Division
Susana Martín Toral
Head of Energy Efficiency Area
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