[en] Increasing trust in energy performance certificates (EPCs) and drawing meaningful conclusions requires a robust and accurate determination of building energy performance (BEP). However, existing and by law prescribed engineering methods, relying on physical principles, are under debate for being error-prone in practice and ultimately inaccurate. Research has heralded data-driven methods, mostly machine learning algorithms, to be promising alternatives: various studies compare engineering and data-driven methods with a clear advantage for data-driven methods in terms of prediction accuracy for BEP. While previous studies only investigated the prediction accuracy for BEP, it yet remains unclear which reasons and cause–effect relationships lead to the surplus prediction accuracy of data-driven methods. In this study, we develop and discuss a theory on how data collection, the type of auditor, the energy quantification method, and its accuracy relate to one another. First, we introduce cause–effect relationships for quantifying BEP method-agnostically and investigate the influence of several design parameters, such as the expertise of the auditor issuing the EPC, to develop our theory. Second, we evaluate and discuss our theory with literature. We find that data-driven methods positively influence cause–effect relationships, compensating for deficits due to auditors’ lack of expertise, leading to high prediction accuracy. We provide recommendations for future research and practice to enable the informed use of data-driven methods.
Disciplines :
Energy Management information systems
Author, co-author :
Wederhake, Lars
Wenninger, Simon
Wiethe, Christian
Fridgen, Gilbert ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
External co-authors :
yes
Language :
English
Title :
On the surplus accuracy of data-driven energy quantification methods in the residential sector
Publication date :
2022
Journal title :
Energy Informatics
ISSN :
2520-8942
Publisher :
SpringerOpen, United Kingdom
Volume :
5
Issue :
1
Pages :
7
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Security, Reliability and Trust Sustainable Development
Ahlrichs J, Rockstuhl S, Tränkler T et al (2020) The impact of political instruments on building energy retrofits: a risk-integrated thermal energy hub approach. Energy Policy 147:111851. 10.1016/j.enpol.2020.111851 DOI: 10.1016/j.enpol.2020.111851
Ahlrichs J, Wenninger S, Wiethe C et al (2022) Impact of socio-economic factors on local energetic retrofitting needs—a data analytics approach. Energy Policy 160:112646. 10.1016/j.enpol.2021.112646 DOI: 10.1016/j.enpol.2021.112646
Ali U, Shamsi MH, Bohacek M et al (2020) A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making. Appl Energy 279:115834. 10.1016/j.apenergy.2020.115834 DOI: 10.1016/j.apenergy.2020.115834
Amasyali K, El-Gohary NM (2018) A review of data-driven building energy consumption prediction studies. Renew Sustain Energy Rev 81:1192–1205. 10.1016/j.rser.2017.04.095 DOI: 10.1016/j.rser.2017.04.095
Amecke H (2012) The impact of energy performance certificates: a survey of German home owners. Energy Policy 46:4–14 DOI: 10.1016/j.enpol.2012.01.064
Andrade-Cabrera C, de Rosa M, Kathirgamanathan A et al (2018) A study on the trade-off between energy forecasting accuracy and computational complexity in lumped parameter building energy models. https://www.researchgate.net/publication/327562414_A_Study_on_the_Trade-off_between_Energy_Forecasting_Accuracy_and_Computational_Complexity_in_Lumped_Parameter_Building_Energy_Models. Accessed 04 Jan 2022
Arcipowska A, Anagnostopoulos F, Mariottini F et al. (2014) Energy performance certificates across the EU. https://bpie.eu/wp-content/uploads/2015/10/Energy-Performance-Certificates-EPC-across-the-EU.-A-mapping-of-national-approaches-2014.pdf. Accessed 04 Jan 2022
Berger J, Orlande HR, Mendes N et al (2016) Bayesian inference for estimating thermal properties of a historic building wall. Build Environ 106:327–339. 10.1016/j.buildenv.2016.06.037 DOI: 10.1016/j.buildenv.2016.06.037
Bevington PR (1969) Data reduction and error analysis for the physical sciences. McGraw Hill Book Co., New York
Bhattacherjee P (2004) Understanding changes in belief and attitude toward information technology usage: a theoretical model and longitudinal test. MIS Q 28:229. 10.2307/25148634 DOI: 10.2307/25148634
Bi J, Zhang T (2005) Support vector classification with input data uncertainty. In: Advances in neural information processing systems. pp 161–168
Bigalke U, Marcinek H (2016) Auswertung von Verbrauchskennwerten energieeffizienter Wohngebäude
Borgstein EH, Lamberts R, Hensen J (2016) Evaluating energy performance in non-domestic buildings: a review. Energy Build 128:734–755. 10.1016/j.enbuild.2016.07.018 DOI: 10.1016/j.enbuild.2016.07.018
Bourdeau M, Xq Z, Nefzaoui E et al (2019) Modeling and forecasting building energy consumption: a review of data-driven techniques. Sustain Cities Soc 48:101533. 10.1016/j.scs.2019.101533 DOI: 10.1016/j.scs.2019.101533
Burman E, Mumovic D, Kimpian J (2014) Towards measurement and verification of energy performance under the framework of the European directive for energy performance of buildings. Energy 77:153–163. 10.1016/j.energy.2014.05.102 DOI: 10.1016/j.energy.2014.05.102
Calì D, Osterhage T, Streblow R et al (2016) Energy performance gap in refurbished German dwellings: lesson learned from a field test. Energy Build 127:1146–1158. 10.1016/j.enbuild.2016.05.020 DOI: 10.1016/j.enbuild.2016.05.020
Claesson J (2011) CERBOF Projekt no. 72: Utfall och metodutvärdering av energideklaration av byggnader. https://www.researchgate.net/publication/237005861_CERBOF_Projekt_no_72_Utfall_och_metodutvardering_av_energideklaration_av_byggnader. Accessed 04 Jan 2022
Coakley D, Raftery P, Keane M (2014) A review of methods to match building energy simulation models to measured data. Renew Sustain Energy Rev 37:123–141. 10.1016/j.rser.2014.05.007 DOI: 10.1016/j.rser.2014.05.007
Davis FD (1985) A technology acceptance model for empirically testing new end-user information systems: theory and results. Ph.D. Thesis
Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13:319. 10.2307/249008 DOI: 10.2307/249008
de Wilde P (2014) The gap between predicted and measured energy performance of buildings: a framework for investigation. Autom Constr 41:40–49. 10.1016/j.autcon.2014.02.009 DOI: 10.1016/j.autcon.2014.02.009
Deb C, Schlueter A (2021) Review of data-driven energy modelling techniques for building retrofit. Renew Sustain Energy Rev 144:110990. 10.1016/j.rser.2021.110990 DOI: 10.1016/j.rser.2021.110990
Deutsche Energie-Agentur GmbH (2016) dena-Gebäudereport: Statistiken und Analysen zur Energieeffizienz im Gebäudebestand
Deutscher Bundestag (2013) Novelle der Energieeinsparverordnung und des Energieeinsparungsgesetzes
Doty DH, Glick WH (1994) Typologies as a unique form of theory building: toward improved understanding and modeling. Acad Manag Rev 19:230. 10.2307/258704 DOI: 10.2307/258704
Droutsa KG, Kontoyiannidis S, Dascalaki EG et al (2016) Mapping the energy performance of hellenic residential buildings from EPC (energy performance certificate) data. Energy 98:284–295. 10.1016/j.energy.2015.12.137 DOI: 10.1016/j.energy.2015.12.137
Eicker U, Zirak M, Bartke N et al (2018) New 3D model based urban energy simulation for climate protection concepts. Energy Build 163:79–91. 10.1016/j.enbuild.2017.12.019 DOI: 10.1016/j.enbuild.2017.12.019
Ettrich M (2008) Rechenverfahren im Wohnungsbau. https://www.regierung.oberbayern.bayern.de/imperia/md/content/regob/internet/dokumente/bereich3/energieeffizientesbauen/veranstaltungen/ettrich_rechenverfahren_wohnungsbau_18_07_2008.pdf. Accessed 26 Aug 2019
European Commission (2020) In focus: energy efficiency in buildings. https://ec.europa.eu/info/news/focus-energy-efficiency-buildings-2020-feb-17_en. Accessed 27 July 2021
European Commission (2021) Making our homes and buildings fit for a greener future. https://ec.europa.eu/commission/presscorner/api/files/attachment/869476/Buildings_Factsheet_EN_final.pdf.pdf. Accessed 27 July 2021
Fernandez I, Borges CE, Penya YK (2011) Efficient building load forecasting. In: ETFA2011. IEEE, pp 1–8
Fornasini P (2008) The uncertainty in physical measurements: an introduction to data analysis in the physics laboratory. Springer, New York DOI: 10.1007/978-0-387-78650-6
Foucquier A, Robert S, Suard F et al (2013) State of the art in building modelling and energy performances prediction: a review. Renew Sustain Energy Rev 23:272–288 DOI: 10.1016/j.rser.2013.03.004
Fox M, Goodhew S, de Wilde P (2016) Building defect detection: external versus internal thermography. Build Environ 105:317–331. 10.1016/j.buildenv.2016.06.011 DOI: 10.1016/j.buildenv.2016.06.011
German Energy Agency (2018) dena Concise building report: energy efficiency in the building stock—statistics and analyses
German Federal Ministry for Economic Affairs and Energy (2018) Energieeffizienz in Zahlen: Entwicklungen und Trends in Deutschland 2018
Gram-Hanssen K (2013) Efficient technologies or user behaviour, which is the more important when reducing households’ energy consumption? Energ Effic 6:447–457. 10.1007/s12053-012-9184-4 DOI: 10.1007/s12053-012-9184-4
Gregor S (2006) The nature of theory in information systems. MIS Q 30:611. 10.2307/25148742 DOI: 10.2307/25148742
Hardy A, Glew D (2019) An analysis of errors in the energy performance certificate database. Energy Policy 129:1168–1178. 10.1016/j.enpol.2019.03.022 DOI: 10.1016/j.enpol.2019.03.022
Heo Y, Choudhary R, Augenbroe GA (2012) Calibration of building energy models for retrofit analysis under uncertainty. Energy Build 47:550–560 DOI: 10.1016/j.enbuild.2011.12.029
Herrando M, Cambra D, Navarro M et al (2016) Energy performance certification of faculty buildings in Spain: the gap between estimated and real energy consumption. Energy Convers Manag 125:141–153. 10.1016/j.enconman.2016.04.037 DOI: 10.1016/j.enconman.2016.04.037
Hertle H, Duscha M, Eisenmann L et al (2005) Verbrauchs- oder Bedarfspass? Anforderungen an den Energiepass für Wohngebäude aus Sicht privater Käufer und Mieter
Kaiser M, Stirnweiß D, Wederhake L (2022) Hierarchische Eignungsprüfung von externen (open) data sets für unternehmensinterne analytics- und machine-learning-Projekte. HMD. 10.1365/s40702-022-00842-3 DOI: 10.1365/s40702-022-00842-3
Kaymakci C, Wenninger S, Sauer A (2021) A holistic framework for AI systems in industrial applications. 16. Internationale Tagung Wirtschaftsinformatik 2021
Klobas JE (1995) Beyond information quality: fitness for purpose and electronic information resource use. J Inf Sci 21:95–114. 10.1177/016555159502100204 DOI: 10.1177/016555159502100204
Lee AS (2001) Editor’s comments. MIS Q
Li Y, Kubicki S, Guerriero A et al (2019) Review of building energy performance certification schemes towards future improvement. Renew Sustain Energy Rev 113:109244. 10.1016/j.rser.2019.109244 DOI: 10.1016/j.rser.2019.109244
Li Q, Ren P, Meng Q (2010) Prediction model of annual energy consumption of residential buildings. In: International conference on advances in energy engineering, pp 223–226
March ST, Smith GF (1995) Design and natural science research on information technology. Decis Support Syst 15:251–266. 10.1016/0167-9236(94)00041-2 DOI: 10.1016/0167-9236(94)00041-2
Mathew PA, Dunn LN, Sohn MD et al (2015) Big-data for building energy performance: lessons from assembling a very large national database of building energy use. Appl Energy 140:85–93. 10.1016/j.apenergy.2014.11.042 DOI: 10.1016/j.apenergy.2014.11.042
Menezes AC, Cripps A, Bouchlaghem D et al (2012) Predicted vs. actual energy performance of non-domestic buildings: using post-occupancy evaluation data to reduce the performance gap. Appl Energy 97:355–364. 10.1016/j.apenergy.2011.11.075 DOI: 10.1016/j.apenergy.2011.11.075
Niehaves B, Ortbach K (2016) The inner and the outer model in explanatory design theory: the case of designing electronic feedback systems. Eur J Inf Syst 25:303–316. 10.1057/ejis.2016.3 DOI: 10.1057/ejis.2016.3
Papadopoulos S, Kontokosta CE (2019) Grading buildings on energy performance using city benchmarking data. Appl Energy 233–234:244–253. 10.1016/j.apenergy.2018.10.053 DOI: 10.1016/j.apenergy.2018.10.053
Pasichnyi O, Wallin J, Levihn F et al (2019) Energy performance certificates—new opportunities for data-enabled urban energy policy instruments? Energy Policy 127:486–499. 10.1016/j.enpol.2018.11.051 DOI: 10.1016/j.enpol.2018.11.051
Pipino LL, Lee YW, Wang RY (2002) Data quality assessment. Commun ACM 45:211–219 DOI: 10.1145/505248.506010
Poel B, van Cruchten G, Balaras CA (2007) Energy performance assessment of existing dwellings. Energy Build 39:393–403. 10.1016/j.enbuild.2006.08.008 DOI: 10.1016/j.enbuild.2006.08.008
Polikar R (2006) Ensemble based systems in decision making. IEEE Circuits Syst Mag 6:21–45. 10.1109/MCAS.2006.1688199 DOI: 10.1109/MCAS.2006.1688199
Pregenzer M, Flotzinger D, Pfurtscheller G (1994) Distinction sensitive learning vector quantisation-a new noise-insensitive classification method. In: The 1994 IEEE international conference on neural networks: IEEE World Congress on Computational Intelligence, June 27–June 29, 1994, Walt Disney World Dolphin Hotel, Orlando Florida. IEEE Neural Networks Council, New York, Piscataway, NJ, pp 2890–2894
Qiao Q, Yunusa-Kaltungo A, Edwards RE (2021) Towards developing a systematic knowledge trend for building energy consumption prediction. J Build Eng 35:101967. 10.1016/j.jobe.2020.101967 DOI: 10.1016/j.jobe.2020.101967
Rockstuhl S, Wenninger S, Wiethe C et al (2021) Understanding the risk perception of energy efficiency investments: investment perspective vs. energy bill perspective. Energy Policy 159:112616. 10.1016/j.enpol.2021.112616 DOI: 10.1016/j.enpol.2021.112616
Semple S, Jenkins D (2020) Variation of energy performance certificate assessments in the European Union. Energy Policy 137:111127. 10.1016/j.enpol.2019.111127 DOI: 10.1016/j.enpol.2019.111127
Sonnenberg C, Vom Brocke J (2011) Evaluation patterns for design science research artefacts. In: European design science symposium. Springer, pp 71–83
Strong DM, Lee YW, Wang RY (1997) Data quality in context. Commun ACM 40:103–110 DOI: 10.1145/253769.253804
Sutherland BR (2020) Driving data into energy-efficient buildings. Joule 4:2256–2258. 10.1016/j.joule.2020.10.017 DOI: 10.1016/j.joule.2020.10.017
The European Parliament and the Council of the European Union (2002) Directive 2002/91/EC of the European Parliament and of the Council of 16 December 2002 on the energy performance of buildings, vol 2002
Tsanas A, Xifara A (2012) Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy Build 49:560–567. 10.1016/j.enbuild.2012.03.003 DOI: 10.1016/j.enbuild.2012.03.003
Walter T, Price PN, Sohn MD (2014) Uncertainty estimation improves energy measurement and verification procedures. Appl Energy 130:230–236. 10.1016/j.apenergy.2014.05.030 DOI: 10.1016/j.apenergy.2014.05.030
Wang RY, Strong DM (1996) Beyond accuracy: what data quality means to data consumers. J Manag Inf Syst 12:5–33 DOI: 10.1080/07421222.1996.11518099
Wang S, Yan C, Xiao F (2012) Quantitative energy performance assessment methods for existing buildings. Energy Build 55:873–888. 10.1016/j.enbuild.2012.08.037 DOI: 10.1016/j.enbuild.2012.08.037
Watson RT, Boudreau MC, Chen AJ (2010) Information systems and environmentally sustainable development: energy informatics and new directions for the IS community. MIS Q 34:23. 10.2307/20721413 DOI: 10.2307/20721413
Wei Y, Zhang X, Shi Y et al (2018) A review of data-driven approaches for prediction and classification of building energy consumption. Renew Sustain Energy Rev 82:1027–1047. 10.1016/j.rser.2017.09.108 DOI: 10.1016/j.rser.2017.09.108
Wenninger S, Wiethe C (2021) Benchmarking energy quantification methods to predict heating energy performance of residential buildings in Germany. Bus Inf Syst Eng. 10.1007/s12599-021-00691-2 DOI: 10.1007/s12599-021-00691-2
Wenninger S, Kaymakci C, Wiethe C (2022a) Explainable long-term building energy consumption prediction using QLattice. Appl Energy 308:118300. 10.1016/j.apenergy.2021.118300 DOI: 10.1016/j.apenergy.2021.118300
Wenninger S, Kaymakci C, Wiethe C et al. (2022b) How sustainable is machine learning in energy applications? The sustainable machine learning balance sheet. In: 17th international conference on Wirtschaftsinformatik, Nürnberg, Germany
Yilmaz E, Aslam JA, Robertson S (2008) A new rank correlation coefficient for information retrieval. In: Chua T-S, Leong M-K, Myaeng SH et al (eds) Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval—SIGIR '08. ACM Press, New York, p 587
Yuan P, Duanmu L, Wang Z (2019) Coal consumption prediction model of space heating with feature selection for rural residences in severe cold area in China. Sustain Cities Soc 50:101643. 10.1016/j.scs.2019.101643 DOI: 10.1016/j.scs.2019.101643
Zhao H, Magoulès F (2012a) A review on the prediction of building energy consumption. Renew Sustain Energy Rev 16:3586–3592. 10.1016/j.rser.2012.02.049 DOI: 10.1016/j.rser.2012.02.049
Zhao H, Magoulès F (2012b) Feature selection for predicting building energy consumption based on statistical learning method. J Algorithms Comput Technol 6:59–77. 10.1260/1748-3018.6.1.59 DOI: 10.1260/1748-3018.6.1.59
