Energy-saving potential prediction models for large-scale building: A state-of-the-art review

Energy-saving potential prediction models play a major role in developing retrofit scheme. Reliable estimation and quantification of energy saving of retrofit measures for these models is essential, since it is often used for guiding political decision-makers. The aim of this paper is to provide up-to-date approaches of predicting energy-saving effect for building retrofit in large-scale, including data-driven, physics-based, and hybrid approaches, while throwing light on workflow and key factors in developing models. The review focuses on pointing out pivotal aspects that are not considered in current models of predicting energy-saving effect for building retrofit in large-scale. It is concluded that the validation of proposed models mainly focuses on an aggregated level, which makes it ignore performance gap differences between buildings. The models exist the problem of prebound- and rebound effects due to uncertainty factor. Occupant's willingness to retrofit is ignored in all three categories of models, which can lead to the prediction result deviate from the actual situation in a certain extent. This paper promotes the development of models for predicting energy-saving potential for large-scale buildings, and help to formulate appropriate strategies for the retrofit of existing buildings.

[1] Ascione F, Bianco N, De Stasio C, Mauro GM, Vanoli GP. CASA, cost-optimal analysis by multi-objective optimisation and artificial neural networks: A new framework for the robust assessment of cost-optimal energy retrofit, feasible for any building. Energy Build 2017;146:200–19.
[2] Berardi U. A cross-country comparison of the building energy consumptions and their trends. Resour Conserv Recycl 2017;123:230–41.
[3] Cabrera Serrenho A, Drewniok M, Dunant C, Allwood JM. Testing the greenhouse gas emissions reduction potential of alternative strategies for the english housing stock. Resour Conserv Recycl 2019;144:267–75.
[4] Asaee SR, Nikoofard S, Ugursal VI, Beausoleil-Morrison I. Techno-economic assessment of photovoltaic (PV) and building integrated photovoltaic/thermal (BIPV/T) system retrofits in the Canadian housing stock. Energy Build 2017;152:667–79.
[5] Li W, Zhou Y, Cetin K, Eom J, Wang Y, Chen G, et al. Modeling urban building energy use: A review of modeling approaches and procedures. Energy 2017;141:2445–57.
[6] Li B, Han S, Wang Y, Li J, Wang Y. Feasibility assessment of the carbon emissions peak in China’s construction industry: Factor decomposition and peak forecast. Sci Total Environ 2020;706.
[7] Ascione F, Bianco N, De Stasio C, Mauro GM, Vanoli GP. Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach. Energy 2017;118:999–1017.
[8] Caputo P, Pasetti G. Overcoming the inertia of building energy retrofit at municipal level: The Italian challenge. Sustain Cities Soc 2015;15:120–34.
[9] Ma Z, Cooper P, Daly D, Ledo L. Existing building retrofits: Methodology and state-of-the-art. Energy Build 2012;55:889–902.
[10] Brøgger M, Bacher P, Madsen H, Wittchen KB. Estimating the influence of rebound effects on the energy-saving potential in building stocks. Energy Build 2018;181:62–74.
[11] Howard B, Parshall L, Thompson J, Hammer S, Dickinson J, Modi V. Spatial distribution of urban building energy consumption by end use. Energy Build 2012;45:141–51.
[12] Ma Z, Cooper P, Daly D, Ledo L. Existing building retrofits: Methodology and state-of-the-art. Energy Build 2012;55:889–902.
[13] Swan LG, Ugursal VI. Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renew Sustain Energy Rev 2009;13:1819–35.
[14] Kavgic M, Mavrogianni A, Mumovic D, Summerfield A, Stevanovic Z, Djurovic-Petrovic M. A review of bottom-up building stock models for energy consumption in the residential sector. Build Environ 2010;45:1683–97.
[15] Suganthi L, Samuel AA. Energy models for demand forecasting - A review. Renew Sustain Energy Rev 2012;16:1223–40.
[16] Zhao HX, Magoulès F. A review on the prediction of building energy consumption. Renew Sustain Energy Rev 2012;16:3586–92.
[17] Foucquier A, Robert S, Suard F, Stéphan L, Jay A. State of the art in building modelling and energy performances prediction: A review. Renew Sustain Energy Rev 2013;23:272–88.
[18] Reinhart CF, Cerezo Davila C. Urban building energy modeling - A review of a nascent field. Build Environ 2016;97:196–202.
[19] Brøgger M, Wittchen KB. Estimating the energy-saving potential in national building stocks – A methodology review. Renew Sustain Energy Rev 2018;82:1489–96.
[20] Ahmad T, Chen H, Guo Y, Wang J. A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: A review. Energy Build 2018;165:301–20.
[21] Bourdeau M, Zhai X qiang, Nefzaoui E, Guo X, Chatellier P. Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustain Cities Soc 2019;48:101533.
[22] Gassar AAA, Cha SH. Energy prediction techniques for large-scale buildings towards a sustainable built environment: A review. Energy Build 2020;224:110238.
[23] Dong B, Li Z, Rahman SMM, Vega R. A hybrid model approach for forecasting future residential electricity consumption. Energy Build 2016;117:341–51.
[24] Pasichnyi O, Wallin J, Kordas O. Data-driven building archetypes for urban building energy modelling. Energy 2019;181:360–77.
[25] Deb C, Schlueter A. Review of data-driven energy modelling techniques for building retrofit. Renew Sustain Energy Rev 2021;144:110990.
[26] Delzendeh E, Wu S, Lee A, Zhou Y. The impact of occupants’ behaviours on building energy analysis: A research review. Renew Sustain Energy Rev 2017;80:1061–71.
[27] Bassani M, Catani L, Cirillo C, Mutani G. Night-time and daytime operating speed distribution in urban arterials. Transp Res Part F Traffic Psychol Behav 2016;42:56–69.
[28] Tardioli G, Kerrigan R, Oates M, O’Donnell J, Finn D. Data driven approaches for prediction of building energy consumption at urban level. Energy Procedia 2015;78:3378–83.
[29] Torabi Moghadam S, Toniolo J, Mutani G, Lombardi P. A GIS-statistical approach for assessing built environment energy use at urban scale. Sustain Cities Soc 2018;37:70–84.
[30] Walter T, Sohn MD. A regression-based approach to estimating retrofit savings using the Building Performance Database. Appl Energy 2016;179:996–1005.
[31] Mastrucci A, Baume O, Stazi F, Leopold U. Estimating energy savings for the residential building stock of an entire city: A GIS-based statistical downscaling approach applied to Rotterdam. Energy Build 2014;75:358–67.
[32] Nino Streicher K, Parra D, Buerer MC, Patel MK. Techno-economic potential of large-scale energy retrofit in the Swiss residential building stock. Energy Procedia 2017;122:121–6.
[33] Wei Y, Zhang X, Shi Y, Xia L, Pan S, Wu J, et al. A review of data-driven approaches for prediction and classification of building energy consumption. Renew Sustain Energy Rev 2018;82:1027–47.
[34] Aydinalp M, Ismet Ugursal V, Fung AS. Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks. Appl Energy 2002;71:87–110.
[35] Hawkins D, Hong SM, Raslan R, Mumovic D, Hanna S. Determinants of energy use in UK higher education buildings using statistical and artificial neural network methods. Int J Sustain Built Environ 2012;1:50–63.
[36] Re Cecconi F, Moretti N, Tagliabue LC. Application of artificial neutral network and geographic information system to evaluate retrofit potential in public school buildings. Renew Sustain Energy Rev 2019;110:266–77.
[37] Park JH, Jeon J, Lee J, Wi S, Yun BY, Kim S. Comparative analysis of the PCM application according to the building type as retrofit system. Build Environ 2019;151:291–302.
[38] Chen Y, Hong T, Piette MA. Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis. Appl Energy 2017;205:323–35.
[39] Kazas G, Fabrizio E, Perino M. Energy demand profile generation with detailed time resolution at an urban district scale: A reference building approach and case study. Appl Energy 2017;193:243–62.
[40] Hens H, Parijs W, Deurinck M. Energy consumption for heating and rebound effects. Energy Build 2010;42:105–10.
[41] Allegrini J, Dorer V, Carmeliet J. Influence of the urban microclimate in street canyons on the energy demand for space cooling and heating of buildings. Energy Build 2012;55:823–32.
[42] Santamouris M, Papanikolaou N, Livada I, Koronakis I, Georgakis C, Argiriou A, et al. On the impact of urban climate on the energy consuption of building. Sol Energy 2001;70:201–16.
[43] Alhamwi A, Medjroubi W, Vogt T, Agert C. GIS-based urban energy systems models and tools: Introducing a model for the optimisation of flexibilisation technologies in urban areas. Appl Energy 2017;191:1–9.
[44] Mata É, Sasic Kalagasidis A, Johnsson F. Building-stock aggregation through archetype buildings: France, Germany, Spain and the UK. Build Environ 2014;81:270–82.
[45] Dall’O’ G, Galante A, Pasetti G. A methodology for evaluating the potential energy savings of retrofitting residential building stocks. Sustain Cities Soc 2012;4:12–21.
[46] Jaffal I, Inard C, Ghiaus C. Fast method to predict building heating demand based on the design of experiments. Energy Build 2009;41:669–77.
[47] Ben H, Steemers K. Modelling energy retrofit using household archetypes. Energy Build 2020;224:110224.
[48] Farahbakhsh H, Ugursal VI, Fung AS. A residential end-use energy consumption model for Canada. Int J Energy Res 1998;22:1133–43.
[49] Dascalaki EG, Droutsa KG, Balaras CA, Kontoyiannidis S. Building typologies as a tool for assessing the energy performance of residential buildings - A case study for the Hellenic building stock. Energy Build 2011;43:3400–9.
[50] V.1.28.1.73 T-K software. No Title 2011.
[51] Torabi Moghadam S, Coccolo S, Mutani G, Lombardi P, Scartezzini JL, Mauree D. A new clustering and visualization method to evaluate urban heat energy planning scenarios. Cities 2019;88:19–36.
[52] Sartori I, Wachenfeldt BJ, Hestnes AG. Energy demand in the Norwegian building stock: Scenarios on potential reduction. Energy Policy 2009;37:1614–27.
[53] Mata É, Kalagasidis AS, Johnsson F. A modelling strategy for energy, carbon, and cost assessments of building stocks. Energy Build 2013;56:100–8.
[54] Mata É, Sasic Kalagasidis A, Johnsson F. Energy usage and technical potential for energy saving measures in the Swedish residential building stock. Energy Policy 2013;55:404–14.
[55] Shimoda Y, Fujii T, Morikawa T, Mizuno M. Residential end-use energy simulation at city scale. Build Environ 2004;39:959–67.
[56] Wang S, Yan C, Xiao F. Quantitative energy performance assessment methods for existing buildings. Energy Build 2012;55:873–88.
[57] Abbasabadi N, Ashayeri M, Azari R, Stephens B, Heidarinejad M. An integrated data-driven framework for urban energy use modeling (UEUM). Appl Energy 2019;253:113550.
[58] Brøgger M, Bacher P, Wittchen KB. A hybrid modelling method for improving estimates of the average energy-saving potential of a building stock. Energy Build 2019;199:287–96.
[59] Nouvel R, Mastrucci A, Leopold U, Baume O, Coors V, Eicker U. Combining GIS-based statistical and engineering urban heat consumption models: Towards a new framework for multi-scale policy support. Energy Build 2015;107:204–12.
[60] Swan LG, Ugursal VI, Beausoleil-Morrison I. Hybrid residential end-use energy and greenhouse gas emissions model - development and verification for Canada. J Build Perform Simul 2012;6:1–23.
[61] Cerezo Davila C, Reinhart CF, Bemis JL. Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets. Energy 2016;117:237–50.
[62] Reames TG. Targeting energy justice: Exploring spatial, racial/ethnic and socioeconomic disparities in urban residential heating energy efficiency. Energy Policy 2016;97:549–58.
[63] Gilani S, O’Brien W, Gunay HB. Simulating occupants’ impact on building energy performance at different spatial scales. Build Environ 2018;132:327–37.
[64] Fabi V, Andersen RV, Corgnati SP, Olesen BW. A methodology for modelling energy-related human behaviour: Application to window opening behaviour in residential buildings. Build Simul 2013;6:415–27. https://doi.org/1227301301196.
[65] Martinaitis V, Zavadskas EK, Motuziene V, Vilutiene T. Importance of occupancy information when simulating energy demand of energy efficient house: A case study. Energy Build 2015;101:64–75.
[66] Baetens R, Saelens D. Modelling uncertainty in district energy simulations by stochastic residential occupant behaviour. J Build Perform Simul 2016;9:431–47.
[67] An J, Yan D, Hong T, Sun K. A novel stochastic modeling method to simulate cooling loads in residential districts. Appl Energy 2017;206:134–49.
[68] Daly D, Cooper P, Ma Z. Implications of global warming for commercial building retrofitting in Australian cities. Build Environ 2014;74:86–95.
[69] Nik VM, Mata É, Sasic Kalagasidis A. A statistical method for assessing retrofitting measures of buildings and ranking their robustness against climate change. Energy Build 2015;88:262–75.
[70] Shen P, Braham W, Yi Y. The feasibility and importance of considering climate change impacts in building retrofit analysis. Appl Energy 2019;233–234:254–70.
[71] Flores-Larsen S, Filippín C, Barea G. Impact of climate change on energy use and bioclimatic design of residential buildings in the 21st century in Argentina. Energy Build 2019;184:216–29.
[72] Hrovatin N, Zorić J. Determinants of energy-efficient home retrofits in Slovenia: The role of information sources. Energy Build 2018;180:42–50.
[73] Gamtessa SF. An explanation of residential energy-efficiency retrofit behavior in Canada. Energy Build 2013;57:155–64.
[74] Jia JJ, Xu JH, Fan Y, Ji Q. Willingness to accept energy-saving measures and adoption barriers in the residential sector: An empirical analysis in Beijing, China. Renew Sustain Energy Rev 2018;95:56–73.
[75] Hsu D. Identifying key variables and interactions in statistical models ofbuilding energy consumption using regularization. Energy 2015;83:144–55.