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

Research output: Book chapterChapter

Abstract

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.
Translated title of the contributionSemantic Data-Driven Models to Improve Energy Efficiency in Buildings and Cities
Original languageEnglish
Title of host publicationHandbook of Research on Developing Smart Cities Based on Digital Twins
EditorsMatteo Del Giudice, Anna Osello
PublisherIGI Global http://www.igi-global.com/
Pages515-539
Number of pages24
ISBN (Print) 9781799870913
DOIs
Publication statusPublished - 1 Jan 2021

Fingerprint

Dive into the research topics of 'Semantic Data-Driven Models to Improve Energy Efficiency in Buildings and Cities'. Together they form a unique fingerprint.

Cite this