- -

A European Database of Building Energy Profiles to Support the Design of Ground Source Heat Pumps

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

A European Database of Building Energy Profiles to Support the Design of Ground Source Heat Pumps

Show simple item record

Files in this item

dc.contributor.author Carnieletto, Laura es_ES
dc.contributor.author Badenes Badenes, Borja es_ES
dc.contributor.author Belliardi, Marco es_ES
dc.contributor.author Bernardi, Adriana es_ES
dc.contributor.author Graci, Samantha es_ES
dc.contributor.author Emmi, Giuseppe es_ES
dc.contributor.author Urchueguía Schölzel, Javier Fermín es_ES
dc.contributor.author Zarrella, Angelo es_ES
dc.contributor.author Di Bella, Antonino es_ES
dc.contributor.author Dalla Santa, Giorgia es_ES
dc.contributor.author Galgaro, Antonio es_ES
dc.contributor.author Mezzasalma, Giulia es_ES
dc.contributor.author De Carli, Michele es_ES
dc.date.accessioned 2020-12-02T04:31:32Z
dc.date.available 2020-12-02T04:31:32Z
dc.date.issued 2019-07-01 es_ES
dc.identifier.uri http://hdl.handle.net/10251/156260
dc.description.abstract [EN] The design of ground source heat pumps is a fundamental step to ensure the high energy efficiency of heat pump systems throughout their operating years. To enhance the diffusion of ground source heat pump systems, two different tools are developed in the H2020 research project named, Cheap GSHPs: A design tool and a decision support system. In both cases, the energy demand of the buildings may not be calculated by the user. The main input data, to evaluate the size of the borehole heat exchangers, is the building energy demand. This paper presents a methodology to correlate energy demand, building typologies, and climatic conditions for different types of residential buildings. Rather than envelope properties, three insulation levels have been considered in different climatic conditions to set up a database of energy profiles. Analyzing European climatic test reference years, 23 locations have been considered. For each location, the overall energy and the mean hourly monthly energy profiles for heating and cooling have been calculated. Pre-calculated profiles are needed to size generation systems and, in particular, ground source heat pumps. For this reason, correlations based on the degree days for heating and cooling demand have been found in order to generalize the results for different buildings. These correlations depend on the Koppen-Geiger climate scale. es_ES
dc.description.sponsorship This work received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 657982. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Energies es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Building energy demand es_ES
dc.subject Energy profiles es_ES
dc.subject GSHP es_ES
dc.subject Residential buildings es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.subject.classification MECANICA DE FLUIDOS es_ES
dc.title A European Database of Building Energy Profiles to Support the Design of Ground Source Heat Pumps es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/en12132496 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/657982/EU/CHEAP AND EFFICIENT APPLICATION OF RELIABLE GROUND SOURCE HEAT EXCHANGERS AND PUMPS/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada es_ES
dc.description.bibliographicCitation Carnieletto, L.; Badenes Badenes, B.; Belliardi, M.; Bernardi, A.; Graci, S.; Emmi, G.; Urchueguía Schölzel, JF.... (2019). A European Database of Building Energy Profiles to Support the Design of Ground Source Heat Pumps. Energies. 12(13):1-23. https://doi.org/10.3390/en12132496 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/en12132496 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 23 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 12 es_ES
dc.description.issue 13 es_ES
dc.identifier.eissn 1996-1073 es_ES
dc.relation.pasarela S\390664 es_ES
dc.description.references De Carli, M., Tonon, M., Zarrella, A., & Zecchin, R. (2010). A computational capacity resistance model (CaRM) for vertical ground-coupled heat exchangers. Renewable Energy, 35(7), 1537-1550. doi:10.1016/j.renene.2009.11.034 es_ES
dc.description.references Grossi, I., Dongellini, M., Piazzi, A., & Morini, G. L. (2018). Dynamic modelling and energy performance analysis of an innovative dual-source heat pump system. Applied Thermal Engineering, 142, 745-759. doi:10.1016/j.applthermaleng.2018.07.022 es_ES
dc.description.references Engineering Reference Manual. In EnergyPlus V8.5https://energyplus.net/ es_ES
dc.description.references Sandberg, N. H., Bergsdal, H., & Brattebø, H. (2011). Historical energy analysis of the Norwegian dwelling stock. Building Research & Information, 39(1), 1-15. doi:10.1080/09613218.2010.528186 es_ES
dc.description.references Application of Energy Performance Indicators for Residential Building Stocks Experiences of the EPISCOPE Projecthttp://episcope.eu/fileadmin/episcope/public/docs/reports/EPISCOPE_Indicators_ConceptAndExperiences.pdf es_ES
dc.description.references Gustafsson, M., Dipasquale, C., Poppi, S., Bellini, A., Fedrizzi, R., Bales, C., … Holmberg, S. (2017). Economic and environmental analysis of energy renovation packages for European office buildings. Energy and Buildings, 148, 155-165. doi:10.1016/j.enbuild.2017.04.079 es_ES
dc.description.references De Carli, M., Bernardi, A., Cultrera, M., Dalla Santa, G., Di Bella, A., Emmi, G., … Zarrella, A. (2018). A Database for Climatic Conditions around Europe for Promoting GSHP Solutions. Geosciences, 8(2), 71. doi:10.3390/geosciences8020071 es_ES
dc.description.references Cartalis, C., Synodinou, A., Proedrou, M., Tsangrassoulis, A., & Santamouris, M. (2001). Modifications in energy demand in urban areas as a result of climate changes: an assessment for the southeast Mediterranean region. Energy Conversion and Management, 42(14), 1647-1656. doi:10.1016/s0196-8904(00)00156-4 es_ES
dc.description.references Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World Map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259-263. doi:10.1127/0941-2948/2006/0130 es_ES
dc.description.references Herrera, M., Natarajan, S., Coley, D. A., Kershaw, T., Ramallo-González, A. P., Eames, M., … Wood, M. (2017). A review of current and future weather data for building simulation. Building Services Engineering Research and Technology, 38(5), 602-627. doi:10.1177/0143624417705937 es_ES
dc.description.references Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences, 11(5), 1633-1644. doi:10.5194/hess-11-1633-2007 es_ES
dc.description.references D’Amico, A., Ciulla, G., Panno, D., & Ferrari, S. (2019). Building energy demand assessment through heating degree days: The importance of a climatic dataset. Applied Energy, 242, 1285-1306. doi:10.1016/j.apenergy.2019.03.167 es_ES
dc.description.references Al-Hadhrami, L. M. (2013). Comprehensive review of cooling and heating degree days characteristics over Kingdom of Saudi Arabia. Renewable and Sustainable Energy Reviews, 27, 305-314. doi:10.1016/j.rser.2013.04.034 es_ES
dc.description.references Degree Days.net-Custom Degree Day Datahttp://www.degreedays.net es_ES
dc.description.references Annunziata, E., Frey, M., & Rizzi, F. (2013). Towards nearly zero-energy buildings: The state-of-art of national regulations in Europe. Energy, 57, 125-133. doi:10.1016/j.energy.2012.11.049 es_ES
dc.description.references Principle for Nearly Zero-Energy Buildings, Ecofys Germany GmbHhttp://bpie.eu/documents/BPIE/publications/LR_nZEB%20study.pdf es_ES
dc.description.references Ahern, C., Griffiths, P., & O’Flaherty, M. (2013). State of the Irish housing stock—Modelling the heat losses of Ireland’s existing detached rural housing stock & estimating the benefit of thermal retrofit measures on this stock. Energy Policy, 55, 139-151. doi:10.1016/j.enpol.2012.11.039 es_ES
dc.description.references Kaklauskas, A., Zavadskas, E. K., Raslanas, S., Ginevicius, R., Komka, A., & Malinauskas, P. (2006). Selection of low-e windows in retrofit of public buildings by applying multiple criteria method COPRAS: A Lithuanian case. Energy and Buildings, 38(5), 454-462. doi:10.1016/j.enbuild.2005.08.005 es_ES
dc.description.references Zavadskas, E., Raslanas, S., & Kaklauskas, A. (2008). The selection of effective retrofit scenarios for panel houses in urban neighborhoods based on expected energy savings and increase in market value: The Vilnius case. Energy and Buildings, 40(4), 573-587. doi:10.1016/j.enbuild.2007.04.015 es_ES
dc.description.references Aerts, D., Minnen, J., Glorieux, I., Wouters, I., & Descamps, F. (2014). A method for the identification and modelling of realistic domestic occupancy sequences for building energy demand simulations and peer comparison. Building and Environment, 75, 67-78. doi:10.1016/j.buildenv.2014.01.021 es_ES
dc.description.references Yang, Z., & Becerik-Gerber, B. (2014). The coupled effects of personalized occupancy profile based HVAC schedules and room reassignment on building energy use. Energy and Buildings, 78, 113-122. doi:10.1016/j.enbuild.2014.04.002 es_ES
dc.description.references Richardson, I., Thomson, M., & Infield, D. (2008). A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings, 40(8), 1560-1566. doi:10.1016/j.enbuild.2008.02.006 es_ES
dc.description.references Villi, G., Peretti, C., Graci, S., & De Carli, M. (2013). Building leakage analysis and infiltration modelling for an Italian multi-family building. Journal of Building Performance Simulation, 6(2), 98-118. doi:10.1080/19401493.2012.699981 es_ES


This item appears in the following Collection(s)

Show simple item record