Implementing User Behaviour on Dynamic Building Simulations for Energy Consumption

[1] International Energy Agency (IEA). CO₂ emissions from fuel combustion Highlights. Paris: IEA, 2013.Search in Google Scholar

[2] EU Climate Action [Online]. Available: https://ec.europa.eu/clima/citizens/eu_enSearch in Google Scholar

[3] Power A. Does demolition or refurbishment of old and inefficient homes help to increase our environmental, social and economic viability? Energy Policy 2008:36(2):4487–4501. doi:10.1016/j.enpol.2008.09.02210.1016/j.enpol.2008.09.022Open DOISearch in Google Scholar

[4] Directive 2012/27/EU of the European Parliament and of the Council of 25 October 2012 on energy efficiency, amending Directives 2009/125/EC and 2010/30/EU and repealing Directives 2004/8/EC and 2006/32/EC. Official Journal of European Union 2012:L315:1–56.Search in Google Scholar

[5] European Commission. European Strategic Energy Technology Plan [Online]. Available: https://ec.europa.eu/jrc/sites/default/files/9170_hi-res.jpgSearch in Google Scholar

[6] ECTP, E2B. Energy-efficient Buildings PPP beyond 2013. Research & Innovation Roadmap. Draft, 2012.Search in Google Scholar

[7] The Buildings Performance Institute Europe BPIE. Implementing the cost-optimal methodology in EU countries, 2013. [Online]. Available: http://bpie.eu/wp-content/uploads/2015/10/Implementing_Cost_Optimality.pdfSearch in Google Scholar

[8] Miezis M., Zvaigznitis K., Stancioff N., Soeftestad L. Climate Change and Buildings Energy Efficiency – the Key Role of Residents. Environmental and Climate Technologies 2016:17(1):30–43. doi:10.1515/rtuect-2016-000410.1515/rtuect-2016-0004Search in Google Scholar

[9] Bourdeau M., Zhai X. Q., Nefzaoui E., Guo X., Chatellier P. Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 2019:48:101533. doi:10.1016/j.scs.2019.10153310.1016/j.scs.2019.101533Search in Google Scholar

[10] Amasyali K., El-Gohary N. M. A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews 2018:81(Part1):1192–1205. doi:10.1016/j.rser.2017.04.09510.1016/j.rser.2017.04.095Search in Google Scholar

[11] Ma Z., Ye C., Li H., Ma W. Applying support vector machines to predict building energy consumption in China. Energy Procedia 2018:152:780–786. doi:10.1016/j.egypro.2018.09.24510.1016/j.egypro.2018.09.245Open DOISearch in Google Scholar

[12] Zhong H., Wang J., Jia H., Mu Y., Lv S. Vector field-based support vector regression for building energy consumption prediction. Applied Energy 2019:242:403–414. doi:10.1016/j.apenergy.2019.03.07810.1016/j.apenergy.2019.03.078Open DOISearch in Google Scholar

[13] Tian C., Li C., Zhang G., Lv Y. Data driven parallel prediction of building energy consumption using generative adversarial nets. Energy and Buildings 2019:186:230–243. doi:10.1016/j.enbuild.2019.01.03410.1016/j.enbuild.2019.01.034Open DOISearch in Google Scholar

[14] Goudarzi S., Anisi M. H., Kama N., Doctor F., Soleymani S. A., Sangaiah A. K. Predictive modelling of building energy consumption based on a hybrid nature-inspired optimization algorithm. Energy and Buildings 2019:196:83–93. doi:10.1016/j.enbuild.2019.05.03110.1016/j.enbuild.2019.05.031Open DOISearch in Google Scholar

[15] Robinson C., Dilkina B., Hubbs J., Zhang W., Guhathakurta S., Brown M. A., Pendyala R. M. Machine learning approaches for estimating commercial building energy consumption. Applied Energy 2017:208:889–904. doi:10.1016/j.apenergy.2017.09.06010.1016/j.apenergy.2017.09.060Open DOISearch in Google Scholar

[16] Wang H., Zhai Z. Advances in building simulation and computational techniques: A review between 1987 and 2014. Energy and Buildings 2016:128:319–335. doi:10.1016/j.enbuild.2016.06.08010.1016/j.enbuild.2016.06.080Open DOISearch in Google Scholar

[17] Smith C. L., Stander J. M., Tyler A. V. Human behaviour incorporation into ecological computer simulations. Environmental Management 1982:6(3):251–260. doi:10.1007/BF0186688810.1007/BF01866888Open DOISearch in Google Scholar

[18] Bariss U., Bazbauers G., Blumberga A., Blumberga D. System Dynamics Modeling of Households’ Electricity Consumption and Cost-Income Ratio: a Case Study of Latvia. Environmental and Climate Technologies 2017:20(1):36–50. doi:10.1515/rtuect-2017-000910.1515/rtuect-2017-0009Open DOISearch in Google Scholar

[19] Allard I., Olofsson T., Nair N. Energy evaluation of residential buildings: Performance gap analysis incorporating uncertainties in the evaluation methods. Building Simulation 2018:11(4):725–737. doi:10.1007/s12273-018-0439-710.1007/s12273-018-0439-7Open DOISearch in Google Scholar

[20] De Wilde P. The gap between predicted and measured energy performance of buildings: A framework for investigation. Automation in Construction 2014:41:40–49. doi:10.1016/j.autcon.2014.02.00910.1016/j.autcon.2014.02.009Open DOISearch in Google Scholar

[21] Biseniece E., Freimanis R., Purvins R., Gravelsins A., Pumpurs A., Blumberga A. Study of Hygrothermal Processes in External Walls with Internal Insulation. Environmental and Climate Technologies 2018:22(1):22–41. doi:10.1515/rtuect-2018-000210.1515/rtuect-2018-0002Open DOISearch in Google Scholar

[22] Monetti V., Davin E., Fabrizio E., André P., Filippi M. Calibration of Building Energy Simulation Models Based on Optimization: A Case Study. Energy Procedia 2015:78:2971–2976. doi:10.1016/j.egypro.2015.11.69310.1016/j.egypro.2015.11.693Open DOISearch in Google Scholar

[23] Hong T., Kim J., Jeong J., Lee M., Ji C. Automatic calibration model of a building energy simulation using optimization algorithm. Energy Procedia 2017:105:3698–3704. doi:10.1016/j.egypro.2017.03.85510.1016/j.egypro.2017.03.855Open DOISearch in Google Scholar

[24] Asadi S., Mostavi E., Boussaa D., Indaganti M. Building energy model calibration using automated optimization-based algorithm. Energy and Buildings 2019:198:106–114. doi:10.1016/j.enbuild.2019.06.00110.1016/j.enbuild.2019.06.001Open DOISearch in Google Scholar

[25] Eguía Oller P., Alonso Rodríguez J. M., Saavedra González A., Arce Fariña E., Granada Álvarez E. Improving the calibration of building simulation with interpolated weather datasets. Renewable Energy 2018:122:608–618. doi:10.1016/j.renene.2018.01.10010.1016/j.renene.2018.01.100Open DOISearch in Google Scholar

[26] Crowther P. Design for Disassembly to Recover Embodied Energy. In Proceedings of the 16th International Conference on Passive and Low-Energy Architecture, Melbourne, Australia, 22–24 September, 1999.Search in Google Scholar

[27] Lutzenhiser L. A cultural model of household energy consumption. Energy 1992:17(1):47–60. doi:10.1016/0360-5442(92)90032-U10.1016/0360-5442(92)90032-Open DOISearch in Google Scholar

[28] Juodis E., Jaraminiene E., Dudkiewicz E. Inherent variability of heat consumption in residential buildings. Energy and Buildings 2009:41(11):1188–1194. doi:10.1016/j.enbuild.2009.06.00710.1016/j.enbuild.2009.06.007Open DOISearch in Google Scholar

[29] Jansson-Boyd C., Robison R., Cloherty R., Jimenez-Bescos C. Complementing retrofit with engagement: exploring energy consumption with social housing tenants. International Journal of Energy Research 2017:41(8):1150–1163. doi:10.1002/er.369810.1002/er.3698Search in Google Scholar

[30] Shippee G. Energy consumption and conservation psychology: A review and conceptual analysis. Environmental Management 1980:4(4):297–314. doi:10.1007/BF0186942310.1007/BF01869423Open DOISearch in Google Scholar

[31] Lammers J. T. H., Berglund L. G., Stolwijk J. A. J. Energy conservation and thermal comfort in a New York city high rise office building. Environmental Management 1978:2(2):113–117. doi:10.1007/BF0186623710.1007/BF01866237Open DOISearch in Google Scholar

[32] Designbuilder software [Online]. Available: http://www.designbuilding.com.Search in Google Scholar

[33] IWEC. International Weather for Energy Calculations, Weather Files: User’s Manual and CD-ROM. Atlanta: ASHRAE, 2001.Search in Google Scholar

[34] U.S. Department of Energy. Input Output Reference: The Encyclopedic Reference to EnergyPlus Input and Output, 2012.Search in Google Scholar

[35] Government of Spain. Ministry of Fomento. Spanish Technical Building Code, basic document DB-HE “Energy saving”, 2013. [Online]. Available: http://www.codigotecnico.org/Search in Google Scholar

[36] Huebner G. M., McMichael M., Shipworth D., Shipworth M., Durand-Daubin M., Summerfield A. The reality of English living rooms, A comparison of internal temperatures against common model assumptions. Energy and Buildings 2013:66:688–696. doi:10.1016/j.enbuild.2013.07.02510.1016/j.enbuild.2013.07.025Open DOISearch in Google Scholar