Semantic Data-Driven Models to Improve Energy Efficiency in Buildings and Cities

Título traducido de la contribución: Semantic Data-Driven Models to Improve Energy Efficiency in Buildings and Cities

Producción científica: Capítulo del libroCapítulo

Resumen

The assessment of building energy performance requires data from multiple domains (energy, architecture, planning, economy) and scales (building, district, city) to be processed with a diversity of applications used by experts from various fields. In order to properly assess the performance of the building stock, and to develop and apply the most effective energy efficiency measures, it is necessary to adopt a comprehensive, holistic approach. In this chapter, three research projects are presented which apply Semantic Web technologies to create energy data models from multiple data sources and domains in order to support decision making in energy efficient building renovation projects: SEMANCO, OptEEmAL, and OPTIMUS. A final reflection on the results achieved in these projects and their links to ongoing research on digital twins is presented.
Título traducido de la contribuciónSemantic Data-Driven Models to Improve Energy Efficiency in Buildings and Cities
Idioma originalInglés
Título de la publicación alojadaHandbook of Research on Developing Smart Cities Based on Digital Twins
EditoresMatteo Del Giudice, Anna Osello
EditorialIGI Global http://www.igi-global.com/
Páginas515-539
Número de páginas24
ISBN (versión impresa) 9781799870913
DOI
EstadoPublicada - 1 ene 2021

Huella

Profundice en los temas de investigación de 'Semantic Data-Driven Models to Improve Energy Efficiency in Buildings and Cities'. En conjunto forman una huella única.

Citar esto