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.
TopBackground
Nowadays, energy related data continue to be spread in numerous proprietary and open data sources whose quality levels and reliability are often questionable. Data are continuously changing, since cities and buildings are dynamic entities in continuous transformation. Moreover, energy related data are heterogeneous since they are generated by different applications used by experts in various fields. However, having continuous access to reliable data sources from multiple domains and applications is essential for the creation of decision support systems based on the integration of such data.
In fact, the lack of reliable information on building energy performance is hindering the widespread application of renovation programmes in cities. To meet the goals set by the Energy Performance of Buildings Directives (EPBD, 2002; EPBD, 2010 & EPBD, 2018) concerning nearly-zero energy buildings (nZEB), not only do all new buildings have to be energy efficient but existing ones must be renovated in order to reach similar standards. However, it is estimated that only 1% of the building stock is renovated each year in Europe (EC, 2020).
Identifying the most appropriate combination of measures to assure a balance between energy performance and investment costs is a key factor for the success of any building renovation project. The renovation process of building stock involves multiple stakeholders – city planners, architectural offices, specialists in various fields, product manufacturers, ESCOs, owners and residents – who operate at different decision realms and interact with each other in various ways during the different stages of the lifecycle of the building (e.g., design, construction, operation). Currently, the lack of decision support systems makes it difficult for stakeholders to make well-informed decisions based on an understanding of the whole complexity involved in the different stages of the building renovation process and follow-up on the consequences of their adoption. In particular, the absence of data on the costs and benefits has been recognized as a major obstacle to overcome in large scale building renovation programmes (Staniaszek et al., 2013). Moreover, the lack of information about the current performance of renovated buildings makes financial institutions, investors and owners reluctant to engage in retrofitting projects (Menassa & Ortiz-Vega, 2013) while private owners do not see the benefits of investing in the renovation of their homes due to the lack of information on the potential benefits in terms of energy costs.
The development of decision support systems which provide multiple stakeholders with the information required to assess and improve building energy performance – for existing and new designs – in an urban context involves: