Abstract
Optimized energy systems are achieved by an increase in energy efficiency in parallel with energy savings. SINFONIA is an FP7 European-funded project that aims to implement smart initiatives for optimized energy systems through deep-energy retrofit of social housing buildings in middle-size European cities (i.e., Bolzano, Italy). From a technical viewpoint, the project’s main challenge is facing retrofit interventions in inhabited flats; from a social one, the challenge is engaging tenants in the project to achieve an effective decrease in energy consumption through a change in energy use, behaviors, and practices. The bridge between technical and social viewpoints is created thanks to an engagement process of tenants that has the support of some tools, such as the smart-energy meter. The involvement of tenants in engagement activities and smart-energy meter interaction must necessarily account for their characteristics as social actors. A thorough description and analysis of tenants’ characteristics is therefore one of the most important starting points in such a research project. The aim of our work is to support experts in the design of smart-energy meters providing them with a methodology for the description and analysis of tenants’ characteristics and social contexts. We perform a cluster analysis on the socio-demographic data of tenants involved in the Bolzano SINFONIA case study, identifying three relevant clusters according to family characteristics. Our future research will focus on the design of smart-energy meters and the development of participatory and learning activities addressed to SINFONIA tenants in order to ensure energy savings.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
Without knowing the right number of clusters a priori, we apply some indices to confirm it, i.e., Hopkins statistics, indices included in NbClust command elaborated in R language (Charrad et al. 2014), and the silhouette cluster plot.
References
Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2014). NbClust: An R package for determining the relevant number of clusters in a data set. Journal of Statistical Software, 61(6), 1–36.
Chirot, D. (1994). How Societies Change (pp. 184). Pine Forge Press.
COP22. (2016, November). Informal Consultation on the First Session of the Conference of the Parties Serving as the Meeting of the Parties to the Paris Agreement (CMA 1) and on Item 4 of the Agenda of COP22.
D’Oca, S., Corgnati, S. P., & Buso, T. (2014). Smart meters and energy savings in Italy: Determining the effectiveness of persuasive communication in dwellings. Energy Research & Social Science, 3, 131–142.
European Community. (2009). Directive 2009/28/EC of the European Parliament and of the Council on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC. Official Journal of the European Union, 60.
European Union. (2012). Directive 2012/27/EU of the European Parliament and of the Council on Energy Efficiency, amending Directive 2009/125/EC and 2010/30/EU and repealing Directives 2004/8/EC and 2006/32/EC. Official Journal of the European Union, 56.
Fischer, C. (2008). Feedback on household electricity consumption: A tool for saving energy? Energy Efficiency, 1(1), 79–104.
Frederiks, E., Stenner, K., & Hobman, E. (2015). The socio-demographic and psychological predictors of residential energy consumption: A comprehensive review. Energies, 8(1), 573–609.
Geelen, D., Reinders, A., & Keyson, D. (2013). Empowering the end-user in smart grids: Recommendations for the design of products and services. Energy Policy, 61, 151–161.
Haas, R., Auer, H., & Biermayr, P. (1998). The impact of consumer behavior on residential energy demand for space heating. Energy and Buildings, 27(2), 195–205.
Hardle, W. K., & Simar, L. (2015). Applied Multivariate Statistical Analysis (p. 800). Berlin, Heidelberg: Springer.
Hargreaves, T., Nye, M., & Burgess, J. (2010). Making energy visible: A qualitative field study of how householders interact with feedback from smart energy monitors. Energy Policy, 38(10), 6111–6119.
Johansson, R. (2003). Case study methodology. In International Conference Methodologies in housing research. Stockholm: Royal Institute of Technology and International Association of People-Environment Studies.
Mietola, R., Miettinen, S., & Vehmas, S. (2017). Presenting and representing others: Towards an ethics of engagement. International Journal of Social Research Methodology, 20(3), 299–309.
Moore, N. (2012). The politics and ethics of naming: Questioning anonymisation in (archival) research. International Journal of Social Research Methodology, 15(4), 331–340.
Moriarty, P., & Honnery, D. (2012). Preparing for a low-energy future. Futures, 44(10), 883–892.
Rösch, C., Bräutigam, K., Kopfmüller, J., Stelzer, V., & Lichtner, P. (2017). Indicator system for the sustainability assessment of the German energy system and its transition. Energy, Sustainability and Society, 7, 13.
Shove, E. (2003). Comfort, cleanliness and convenience: the social organization of normality (p. 240). Berg Publishers.
Unfccc. (2015). Paris Agreement (22nd April 2016).
Whitmarsh, L., O’Neill, S., & Lorenzoni, I. (2011). Engaging the public with climate change: Behaviour change and communication (p. 288). Routledge.
Zografakis, N., Menegaki, A. N., & Tsagarakis, K. P. (2008). Effective education for energy efficiency. Energy Policy, 36(8), 3226–3232.
Acknowledgements
We are thankful to Dr. Petra Scudo (EURAC research), who edited the text of the present investigation.
Work described in this paper is funded by the SINFONIA project which has received funding from the European Union’s Seventh Framework Programme for research, technological development, and demonstration under grant agreement No. 609019. The European Union is not liable for any use that may be made of the information contained in this document which is merely representing the authors’ view.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Balest, J., Vettorato, D. (2018). Social Acceptance of Energy Retrofit in Social Housing: Beyond the Technological Viewpoint. In: Bisello, A., Vettorato, D., Laconte, P., Costa, S. (eds) Smart and Sustainable Planning for Cities and Regions. SSPCR 2017. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-75774-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-75774-2_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75773-5
Online ISBN: 978-3-319-75774-2
eBook Packages: EnergyEnergy (R0)