Skip to main content
SpringerLink
Log in

Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities: Current trends and future outlook

  • Review Article
  • Advances in Modeling and Simulation Tools
  • Published:
Building Simulation Aims and scope Submit manuscript

Abstract

Energy demand fluctuations due to low probability high impact (LPHI) micro-climatic events such as urban heat island effect (UHI) and heatwaves, pose significant challenges for urban infrastructure, particularly within urban built-clusters. Mapping short term load forecasting (STLF) of buildings in urban micro-climatic setting (UMS) is obscured by the complex interplay of surrounding morphology, micro-climate and inter-building energy dynamics. Conventional urban building energy modelling (UBEM) approaches to provide quantitative insights about building energy consumption often neglect the synergistic impacts of micro-climate and urban morphology in short temporal scale. Reduced order modelling, unavailability of rich urban datasets such as building key performance indicators for building archetypes-characterization, limit the inter-building energy dynamics consideration into UBEMs. In addition, mismatch of resolutions of spatio–temporal datasets (meso to micro scale transition), LPHI events extent prediction around UMS as well as its accurate quantitative inclusion in UBEM input organization step pose another degree of limitations. This review aims to direct attention towards an integrated-UBEM (i-UBEM) framework to capture the building load fluctuation over multi-scale spatio–temporal scenario. It highlights usage of emerging data-driven hybrid approaches, after systematically analysing developments and limitations of recent physical, data-driven artificial intelligence and machine learning (AI-ML) based modelling approaches. It also discusses the potential integration of google earth engine (GEE)-cloud computing platform in UBEM input organization step to (i) map the land surface temperature (LST) data (quantitative attribute implying LPHI event occurrence), (ii) manage and pre-process high-resolution spatio–temporal UBEM input-datasets. Further the potential of digital twin, central structed data models to integrate along UBEM workflow to reduce uncertainties related to building archetype characterizations is explored. It has also found that a trade-off between high-fidelity baseline simulation models and computationally efficient platform support or co-simulation platform integration is essential to capture LPHI induced inter-building energy dynamics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abbasabadi N, Azari R (2019). A data-driven framework for urban building operational energy use modeling. In: Proceeding of the 10th Annual Symposium on Simulation for Architecture and Urban Design (SimAUD 2019), Atlanta, USA.

  • Abunnasr Y, Mhawej M, Chrysoulakis N (2022). SEBU: A novel fully automated Google Earth Engine surface energy balance model for urban areas. Urban Climate, 44: 101187.

    Article  Google Scholar 

  • Adilkhanova I, Ngarambe J, Yun GY (2022). Recent advances in black box and white-box models for urban heat island prediction: implications of fusing the two methods. Renewable and Sustainable Energy Reviews, 165: 112520.

    Article  Google Scholar 

  • Al Kafy A, Faisal AA, Rahman MS, et al. (2021). Prediction of seasonal urban thermal field variance index using machine learning algorithms in Cumilla, Bangladesh. Sustainable Cities and Society, 64: 102542.

    Article  Google Scholar 

  • Ali U, Shamsi MH, Hoare C, et al. (2021). Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis. Energy and Buildings, 246: 111073.

    Article  Google Scholar 

  • Ali U, Bano S, Shamsi MH, et al. (2024). Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach. Energy and Buildings, 303: 113768.

    Article  Google Scholar 

  • Allegrini J, Carmeliet J (2018). Simulations of local heat islands in Zürich with coupled CFD and building energy models. Urban Climate, 24: 340–359.

    Article  Google Scholar 

  • Amani M, Mahdavi S, Afshar M, et al. (2019). Canadian Wetland Inventory using Google Earth Engine: the First Map and Preliminary Results. Remote Sensing, 11: 842.

    Article  Google Scholar 

  • Ang Y, Berzolla ZM, Reinhart CF (2020). From concept to application: A review of use cases in urban building energy modeling. Applied Energy, 279: 115738.

    Article  Google Scholar 

  • Arantes L, Bucchianeri X, Quenard D, et al. (2014). Étude énergétique d’une conception rationalisée de la façade: le concept Core-Skin-Shell. In: Proceedings of Conférence Francophone IBPSA France 2014. (in French)

  • Araujo G, Santos L, Leitão A, et al. (2022). AD-Based Surrogate Models for Simulation and Optimization of Large Urban Areas. In: Proceedings of the 27th Conference on Computer Aided Architectural Design Research in Asia (CAADRIA), Volume 2, Sydney, Australia.

  • Bao J, Xu L, Shi Y, et al. (2023). The influence of street morphology on thermal environment based on ENVI-met simulation: a case study of Hangzhou core area, China. ISPRS International Journal of Geo-Information, 12: 303.

    Article  Google Scholar 

  • Battini F, Pernigotto G, Gasparella A (2023). A shoeboxing algorithm for urban building energy modeling: Validation for stand-alone buildings. Sustainable Cities and Society, 89: 104305.

    Article  Google Scholar 

  • Bourdeau M, Zhai X, Nefzaoui E, et al. (2019). Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society, 48: 101533.

    Article  Google Scholar 

  • Bruse M, Fleer H (1998). Simulating surface–plant–air interactions inside urban environments with a three dimensional numerical model. Environmental Modelling & Software, 13: 373–384.

    Article  Google Scholar 

  • Bueno B, Norford L, Pigeon G, et al. (2012). A resistance-capacitance network model for the analysis of the interactions between the energy performance of buildings and the urban climate. Building and Environment, 54: 116–125.

    Article  Google Scholar 

  • Bueno B, Norford L, Hidalgo J, et al. (2013). The urban weather generator. Journal of Building Performance Simulation, 6: 269–281.

    Article  Google Scholar 

  • Campos-Taberner M, Moreno-Martínez Á, García-Haro F, et al. (2018). Global estimation of biophysical variables from google earth engine platform. Remote Sensing, 10: 1167.

    Article  Google Scholar 

  • Castaldo VL, Pisello AL, Piselli C, et al. (2018). How outdoor microclimate mitigation affects building thermal-energy performance: a new design-stage method for energy saving in residential near-zero energy settlements in Italy. Renewable Energy, 127: 920–935.

    Article  Google Scholar 

  • Cerezo Davila C, Reinhart CF, Bemis JL (2016). Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets. Energy, 117: 237–250.

    Article  Google Scholar 

  • Chalal ML, Benachir M, White M, et al. (2016). Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: A review. Renewable and Sustainable Energy Reviews, 64: 761–776.

    Article  Google Scholar 

  • Chen Y, Xu P, Chu Y, et al. (2017). Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings. Applied Energy, 195: 659–670.

    Article  Google Scholar 

  • Chen J, Adhikari R, Wilson E, et al. (2022a). Stochastic simulation of occupant-driven energy use in a bottom-up residential building stock model. Applied Energy, 325: 119890.

    Article  Google Scholar 

  • Chen Y, Guo M, Chen Z, et al. (2022b). Physical energy and data-driven models in building energy prediction: A review. Energy Reports, 8: 2656–2671.

    Article  Google Scholar 

  • Chen G, Chen Y, He H, et al. (2023). Assessing the synergies between heat waves and urban heat islands of different local climate zones in Guangzhou, China. Building and Environment, 240: 110434.

    Article  Google Scholar 

  • Cheshmehzangi A, He B-J, Sharifi A, et al. (2023). Climate Change and Cooling Cities. Singapore: Springer Singapore.

    Book  Google Scholar 

  • Chu Y, Xu P, Li M, et al. (2020). Short-term metropolitan-scale electric load forecasting based on load decomposition and ensemble algorithms. Energy and Buildings, 225: 110343.

    Article  Google Scholar 

  • Colelli FP, Wing IS, De Cian E (2023). Air-conditioning adoption and electricity demand highlight climate change mitigation–adaptation tradeoffs. Scientific Reports, 13: 4413.

    Article  Google Scholar 

  • Cortes A, Rejuso AJ, Santos JA, et al. (2022). Evaluating mitigation strategies for urban heat island in Mandaue City using ENVI-Met. Journal of Urban Management, 11: 97–106.

    Article  Google Scholar 

  • Costa CJ, Tiwari S, Bhagat K, et al. (2021). Three-Dimensional Reconstruction of Satellite images using Generative Adversarial Networks. In: Proceedings of 2021 International Conference on Technological Advancements and Innovations (ICTAI), Tashkent, Uzbekistan.

  • Crawley DB, Hand JW, Lawrie LK (1999). Improving the weather information available to simulation programs. In: Proceedings of the 6th International IBPSA Building Simulation Conference, Kyoto, Japan.

  • Cui F, Hamdi R, Kuang W, et al. (2023). Interactions between the summer urban heat islands and heat waves in Beijing during 2000–2018. Atmospheric Research, 291: 106813.

    Article  Google Scholar 

  • Dahlström L, Broström T, Widén J (2022). Advancing urban building energy modelling through new model components and applications: A review. Energy and Buildings, 266: 112099.

    Article  Google Scholar 

  • Dawood N, Dawood H, Rodriguez-Trejo S, et al. (2017). Visualising urban energy use: the use of LiDAR and remote sensing data in urban energy planning. Visualization in Engineering, 5: 22.

    Article  Google Scholar 

  • de Lemos Martins TA, Faraut S, Adolphe L (2019). Influence of context-sensitive urban and architectural design factors on the energy demand of buildings in Toulouse, France. Energy and Buildings, 190: 262–278.

    Article  Google Scholar 

  • Demuzere M, Kittner J, Martilli A, et al. (2022). A Global map of local climate zones to support earth system modelling and urban-scale environmental science. Earth System Science Data, 14: 3835–3873.

    Article  Google Scholar 

  • Dogan T, Reinhart C (2017). Shoeboxer: an algorithm for abstracted rapid multi-zone urban building energy model generation and simulation. Energy and Buildings, 140: 140–153.

    Article  Google Scholar 

  • Dougherty TR, Jain RK (2023a). Invisible walls: Exploration of microclimate effects on building energy consumption in New York City. Sustainable Cities and Society, 90: 104364.

    Article  Google Scholar 

  • Dougherty TR, Jain RK (2023b). TOM.D: Taking advantage of microclimate data for urban building energy modeling. Advances in Applied Energy, 10: 100138.

    Article  Google Scholar 

  • Fan J, Yue W, Wu L, et al. (2018). Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China. Agricultural and Forest Meteorology, 263: 225–241.

    Article  Google Scholar 

  • Ferrando M, Causone F, Hong T, et al. (2020). Urban building energy modeling (UBEM) tools: A state-of-the-art review of bottom-up physics-based approaches. Sustainable Cities and Society, 62: 102408.

    Article  Google Scholar 

  • Fonseca JA, Schlueter A (2015). Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts. Applied Energy, 142: 247–265.

    Article  Google Scholar 

  • Garlík B (2022). Energy sustainability of a cluster of buildings with the application of smart grids and the decentralization of renewable energy sources. Energies, 15: 1649.

    Article  Google Scholar 

  • Geng X, Zhang D, Li C, et al. (2023). Impacts of climatic zones on urban heat island: Spatiotemporal variations, trends, and drivers in China from 2001–2020. Sustainable Cities and Society, 89: 104303.

    Article  Google Scholar 

  • Gobakis K, Kolokotsa D (2017). Coupling building energy simulation software with microclimatic simulation for the evaluation of the impact of urban outdoor conditions on the energy consumption and indoor environmental quality. Energy and Buildings, 157: 101–115.

    Article  Google Scholar 

  • González-Torres M, Pérez-Lombard L, Coronel JF, et al. (2022). A review on buildings energy information: Trends, end-uses, fuels and drivers. Energy Reports, 8: 626–637.

    Article  Google Scholar 

  • Gros A, Bozonnet E, Inard C (2014). Cool materials impact at district scale—Coupling building energy and microclimate models. Sustainable Cities and Society, 13: 254–266.

    Article  Google Scholar 

  • He X, Gao W, Wang R (2021). Impact of urban morphology on the microclimate around elementary schools: A case study from Japan. Building and Environment, 206: 108383.

    Article  Google Scholar 

  • He B, Wang W, Sharifi A, et al. (2023). Progress, knowledge gap and future directions of urban heat mitigation and adaptation research through a bibliometric review of history and evolution. Energy and Buildings, 287: 112976.

    Article  Google Scholar 

  • Hedegaard RE, Kristensen MH, Pedersen TH, et al. (2019). Bottom-up modelling methodology for urban-scale analysis of residential space heating demand response. Applied Energy, 242: 181–204.

    Article  Google Scholar 

  • Heidenthaler D, Deng Y, Leeb M, et al. (2023). Automated energy performance certificate based urban building energy modelling approach for predicting heat load profiles of districts. Energy, 278: 128024.

    Article  Google Scholar 

  • Hensen JLM, Lamberts R (2011). Building Performance Simulation for Design and Operation: Building Performance Simulating for Design and Operation. London: Routledge.

    Google Scholar 

  • Hong T, Chen Y, Luo X, et al. (2020). Ten questions on urban building energy modeling. Building and Environment, 168: 106508.

    Article  Google Scholar 

  • Hong T, Xu Y, Sun K, et al. (2021). Urban microclimate and its impact on building performance: A case study of San Francisco. Urban Climate, 38: 100871.

    Article  Google Scholar 

  • Hong T, Malik J, Krelling A, et al. (2023). Ten questions concerning thermal resilience of buildings and occupants for climate adaptation. Building and Environment, 244: 110806.

    Article  Google Scholar 

  • Hosseini M, Javanroodi K, Nik VM (2022). High-resolution impact assessment of climate change on building energy performance considering extreme weather events and microclimate–Investigating variations in indoor thermal comfort and degree-days. Sustainable Cities and Society, 78: 103634.

    Article  Google Scholar 

  • Hou D, Hassan IG, Wang L (2021). Review on building energy model calibration by Bayesian inference. Renewable and Sustainable Energy Reviews, 143: 110930.

    Article  Google Scholar 

  • Howard B, Parshall L, Thompson J, et al. (2012). Spatial distribution of urban building energy consumption by end use. Energy and Buildings, 45: 141–151.

    Article  Google Scholar 

  • Hu Y, Dai Z, Guldmann JM (2020). Modeling the impact of 2D/3D urban indicators on the urban heat island over different seasons: a boosted regression tree approach. Journal of Environmental Management, 266: 110424.

    Article  Google Scholar 

  • Huang J, Jones P, Zhang A, et al. (2020). Urban Building Energy and Climate (UrBEC) simulation: example application and field evaluation in Sai Ying Pun, Hong Kong. Energy and Buildings, 207: 109580.

    Article  Google Scholar 

  • Huang X, Wang Y (2019). Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: a case study of Wuhan, Central China. ISPRS Journal of Photogrammetry and Remote Sensing, 152: 119–131.

    Article  Google Scholar 

  • Idrissi Kaitouni S, Pfafferott J, Jamil A, et al. (2023). A holistic digital workflow methodology to shifting towards net zero energy urban residential buildings in a semi-arid climate. Solar Energy, 263: 111959.

    Article  Google Scholar 

  • Johari F, Peronato G, Sadeghian P, et al. (2020). Urban building energy modeling: State of the art and future prospects. Renewable and Sustainable Energy Reviews, 128: 109902.

    Article  Google Scholar 

  • Kamal A, Abidi SMH, Mahfouz A, et al. (2021). Impact of urban morphology on urban microclimate and building energy loads. Energy and Buildings, 253: 111499.

    Article  Google Scholar 

  • Kamal A, Mahfouz A, Sezer N, et al. (2023). Investigation of urban heat island and climate change and their combined impact on building cooling demand in the hot and humid climate of Qatar. Urban Climate, 52: 101704.

    Article  Google Scholar 

  • Kamel E (2022). A systematic literature review of physics-based urban building energy modeling (UBEM) tools, data sources, and challenges for energy conservation. Energies, 15: 8649.

    Article  Google Scholar 

  • Keirstead J, Jennings M, Sivakumar A (2012). A review of urban energy system models: Approaches, challenges and opportunities. Renewable and Sustainable Energy Reviews, 16: 3847–3866.

    Article  Google Scholar 

  • Kersten TP, Keller F, Jerome Saenger J, et al. (2012). Automated generation of an historic 4D city model of hamburg and its visualisation with the GE engine. In: Ioannides M, Fritsch D, Leissner J, et al. (eds), Progress in Cultural Heritage Preservation. EuroMed 2012. Lecture Notes in Computer Science, vol 7616. Berlin: Springer Berlin Heidelberg.

    Google Scholar 

  • Keshtkarbanaeemoghadam A, Dehghanbanadaki A, Kaboli MH (2018). Estimation and optimization of heating energy demand of a mountain shelter by soft computing techniques. Sustainable Cities and Society, 41: 728–748.

    Article  Google Scholar 

  • Kikegawa Y, Genchi Y, Yoshikado H, et al. (2003). Development of a numerical simulation system toward comprehensive assessments of urban warming countermeasures including their impacts upon the urban buildings’ energy-demands. Applied Energy, 76: 449–466.

    Article  Google Scholar 

  • Lafore JP, Stein J, Asencio N, et al. (1997). The Meso-NH Atmospheric Simulation System. Part I: Adiabatic formulation and control simulations. Annales Geophysicae, 16: 90–109.

    Article  Google Scholar 

  • Lauzet N, Rodler A, Musy M, et al. (2019). How building energy models take the local climate into account in an urban context—A review. Renewable and Sustainable Energy Reviews, 116: 109390.

    Article  Google Scholar 

  • Li D, Bou-Zeid E (2013). Synergistic interactions between urban heat islands and heat waves: The impact in cities is larger than the sum of its parts. Journal of Applied Meteorology and Climatology, 52: 2051–2064.

    Article  Google Scholar 

  • Li Q, Quan SJ, Augenbroe G, et al. (2015). Building energy modelling at urban scale: Integration of reduced order energy model with geographical information. In: Proceedings of the 14th International IBPSA Building Simulation Conference, Hyderabad, India.

  • Li H, Zhou Y, Wang X, et al. (2019a). Quantifying urban heat island intensity and its physical mechanism using WRF/UCM. Science of the Total Environment, 650: 3110–3119.

    Article  Google Scholar 

  • Li X, Zhou Y, Yu S, et al. (2019b). Urban heat island impacts on building energy consumption: A review of approaches and findings. Energy, 174: 407–419.

    Article  Google Scholar 

  • Li Q, Wang W, Yu Z, et al. (2023a). Assessing urban micro-climates with vertical and horizontal building morphological cutting deep transfer learning neural networks. Building and Environment, 234: 110186.

    Article  Google Scholar 

  • Li Z, Ma J, Tan Y, et al. (2023b). Combining physical approaches with deep learning techniques for urban building energy modeling: A comprehensive review and future research prospects. Building and Environment, 246: 110960.

    Article  Google Scholar 

  • Lin Z, Hong T, Xu X, et al. (2023). Evaluating energy retrofits of historic buildings in a university campus using an urban building energy model that considers uncertainties. Sustainable Cities and Society, 95: 104602.

    Article  Google Scholar 

  • Liu S, Kwok YT, Ren C (2023). Investigating the impact of urban microclimate on building thermal performance: A case study of dense urban areas in Hong Kong. Sustainable Cities and Society, 94: 104509.

    Article  Google Scholar 

  • Luo XJ, Oyedele LO, Ajayi AO, et al. (2020). Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings. Renewable and Sustainable Energy Reviews, 131: 109980.

    Article  Google Scholar 

  • Luo N, Luo X, Mortezazadeh M, et al. (2022). A data schema for exchanging information between urban building energy models and urban microclimate models in coupled simulations. Journal of Building Performance Simulation, https://doi.org/10.1080/19401493.2022.2142295

  • Malhotra A, Bischof J, Nichersu A, et al. (2022). Information modelling for urban building energy simulation—A taxonomic review. Building and Environment, 208: 108552.

    Article  Google Scholar 

  • Manandhar P, Rafiq H, Rodriguez-Ubinas E (2023). Current status, challenges, and prospects of data-driven urban energy modeling: A review of machine learning methods. Energy Reports, 9: 2757–2776.

    Article  Google Scholar 

  • Martilli A, Clappier A, Rotach MW (2002). An urban surface exchange parameterisation for mesoscale models. Boundary-Layer Meteorology, 104: 261–304.

    Article  Google Scholar 

  • Mathew A, Sarwesh P, Khandelwal S (2022). Investigating the contrast diurnal relationship of land surface temperatures with various surface parameters represent vegetation, soil, water, and urbanization over Ahmedabad city in India. Energy Nexus, 5: 100044.

    Article  Google Scholar 

  • Mauree D, Coccolo S, Perera A, et al. (2018). A new framework to evaluate urban design using urban microclimatic modeling in future climatic conditions. Sustainability, 10: 1134.

    Article  Google Scholar 

  • Mavrogianni A, Davies M, Kolokotroni M, et al. (2009). A GIS-based bottom-up space heating demand model of the London domestic stock. In: Proceedings of the 7th International IBPSA Building Simulation Conference, Glasgow, UK.

  • Meng F, Zhang L, Ren G, et al. (2023). Impacts of UHI on variations in cooling loads in buildings during heatwaves: A case study of Beijing and Tianjin, China. Energy, 273: 127189.

    Article  Google Scholar 

  • Miller C, Thomas D, Kämpf J, et al. (2018). Urban and building multiscale co-simulation: case study implementations on two university campuses. Journal of Building Performance Simulation, 11: 309–321.

    Article  Google Scholar 

  • Mohammadi N, Taylor JE (2017). Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction. Applied Energy, 195: 810–818.

    Article  Google Scholar 

  • Mohammed A, Khan A, Santamouris M (2021). On the mitigation potential and climatic impact of modified urban albedo on a subtropical desert city. Building and Environment, 206: 108276.

    Article  Google Scholar 

  • Mohammed A, Khan A, Santamouris M (2023). Numerical evaluation of enhanced green infrastructures for mitigating urban heat in a desert urban setting. Building Simulation, 16: 1691–1712.

    Article  Google Scholar 

  • Mutani G, Carozza M, Todeschi V, et al. (2020). Urban-Scale Energy Models: Relationship between urban form and energy performance. In: Proceedings of the 3rd International Conference and Workshop in Óbuda on Electrical and Power Engineering (CANDO-EPE), Budapest, Hungary.

  • Mutani G, Vocale P, Javanroodi K (2023). Toward improved urban building energy modeling using a place-based approach. Energies, 16: 3944.

    Article  Google Scholar 

  • Neto-Bradley AP, Choudhary R, Challenor P (2023). A microsimulation of spatial inequality in energy access: a Bayesian multi-level modelling approach for urban India. Environment and Planning B: Urban Analytics and City Science, 50: 895–910.

    Google Scholar 

  • Nik VM, Perera ATD, Chen D (2021). Towards climate resilient urban energy systems: A review. National Science Review, 8: nwaa134.

    Article  Google Scholar 

  • Nouvel R, Mastrucci A, Leopold U, et al. (2015). Combining GIS-based statistical and engineering urban heat consumption models: Towards a new framework for multi-scale policy support. Energy and Buildings, 107: 204–212.

    Article  Google Scholar 

  • Nutkiewicz A, Yang Z, Jain RK (2018). Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow. Applied Energy, 225: 1176–1189.

    Article  Google Scholar 

  • Nutkiewicz A, Choi B, Jain RK (2021). Exploring the influence of urban context on building energy retrofit performance: A hybrid simulation and data-driven approach. Advances in Applied Energy, 3: 100038.

    Article  Google Scholar 

  • Oke TR (1976). The distinction between canopy and boundary-layer urban heat islands. Atmosphere, 14: 268–277.

    Article  Google Scholar 

  • Oliveira A, Lopes A, Niza S, et al. (2022). An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: A heatwave event in Naples. The Science of the Total Environment, 805: 150130.

    Article  Google Scholar 

  • Omrany H, Ghaffarianhoseini A, Ghaffarianhoseini A, et al. (2023). The uptake of City Information Modelling (CIM): A comprehensive review of current implementations, challenges and future outlook. Smart and Sustainable Built Environment, 12: 1090–1116.

    Article  Google Scholar 

  • Park Y, Guldmann JM (2019). Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach. Computers, Environment and Urban Systems, 75: 76–89.

    Article  Google Scholar 

  • Park K, Jin HG, Baik JJ (2023). Contrasting interactions between urban heat islands and heat waves in Seoul, South Korea, and their associations with synoptic patterns. Urban Climate, 49: 101524.

    Article  Google Scholar 

  • Pei L, Schalbart P, Peuportier B (2023). Quantitative evaluation of the effects of heat island on building energy simulation: A case study in Wuhan, China. Energies, 16: 3032.

    Article  Google Scholar 

  • Pejovic A, Trane M, Giovanardi M, et al. (2023). On the role of urban mobility layout against urban microclimate: The case study of Turin, Italy. Preprints. Available at https://doi.org/10.20944/PREPRINTS202306.1522.V1.

  • Pena Acosta M, Vahdatikhaki F, Santos J, et al. (2021). How to bring UHI to the urban planning table? A data-driven modeling approach. Sustainable Cities and Society, 71: 102948.

    Article  Google Scholar 

  • Perera ATD, Coccolo S, Scartezzini JL, et al. (2018). Quantifying the impact of urban climate by extending the boundaries of urban energy system modeling. Applied Energy, 222: 847–860.

    Article  Google Scholar 

  • Perera ATD, Nik VM, Chen D, et al. (2020). Quantifying the impacts of climate change and extreme climate events on energy systems. Nature Energy, 5: 150–159.

    Article  Google Scholar 

  • Perera ATD, Javanroodi K, Mauree D, et al. (2023). Challenges resulting from urban density and climate change for the EU energy transition. Nature Energy, 8: 397–412.

    Article  Google Scholar 

  • Prataviera E, Vivian J, Lombardo G, et al. (2022). Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis. Applied Energy, 311: 118691.

    Article  Google Scholar 

  • Reinhart CF, Cerezo Davila C (2016). Urban building energy modeling—A review of a nascent field. Building and Environment, 97: 196–202.

    Article  Google Scholar 

  • Ren Z, Paevere P, McNamara C (2012). A local-community-level, physically-based model of end-use energy consumption by Australian housing stock. Energy Policy, 49: 586–596.

    Article  Google Scholar 

  • Ribeiro M, Grolinger K, ElYamany HF, et al. (2018). Transfer learning with seasonal and trend adjustment for cross-building energy forecasting. Energy and Buildings, 165: 352–363.

    Article  Google Scholar 

  • Robinson D (2011). Computer Modelling for Sustainable Urban Design: Physical Principles, Methods and Applications. London: Routledge.

    Google Scholar 

  • Romano P, Prataviera E, Carnieletto L, et al. (2021). Assessment of the urban heat island impact on building energy performance at district level with the EUReCA platform. Climate, 9: 48.

    Article  Google Scholar 

  • Saeed Khan H, Paolini R, Caccetta P, et al. (2022). On the combined impact of local, regional, and global climatic changes on the urban energy performance and indoor thermal comfort—The energy potential of adaptation measures. Energy and Buildings, 267: 112152.

    Article  Google Scholar 

  • Salamanca F, Martilli A, Tewari M, et al. (2011). A study of the urban boundary layer using different urban parameterizations and high-resolution urban canopy parameters with WRF. Journal of Applied Meteorology and Climatology, 50: 1107–1128.

    Article  Google Scholar 

  • Salim FD, Dong B, Ouf M, et al. (2020). Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey. Building and Environment, 183: 106964.

    Article  Google Scholar 

  • Seyedzadeh S, Pour Rahimian F, Rastogi P, et al. (2019). Tuning machine learning models for prediction of building energy loads. Sustainable Cities and Society, 47: 101484.

    Article  Google Scholar 

  • Sezer N, Yoonus H, Zhan D, et al. (2023). Urban microclimate and building energy models: a review of the latest progress in coupling strategies. Renewable and Sustainable Energy Reviews, 184: 113577.

    Article  Google Scholar 

  • Shirzadi M, Naghashzadegan M, Mirzaei PA (2018). Improving the CFD modelling of cross-ventilation in highly-packed urban areas. Sustainable Cities and Society, 37: 451–465.

    Article  Google Scholar 

  • Shirzadi M, Naghashzadegan M, Mirzaei PA (2019). Developing a framework for improvement of building thermal performance modeling under urban microclimate interactions. Sustainable Cities and Society, 44: 27–39.

    Article  Google Scholar 

  • Singh M, Sharston R (2022). Quantifying the dualistic nature of urban heat Island effect (UHI) on building energy consumption. Energy and Buildings, 255: 111649.

    Article  Google Scholar 

  • Singh M, Sharston R (2023). Normalized urban heat island (UHI) indicators: Classifying the temporal variation of UHI for building energy simulation (BES) applications. Building Simulation, 16: 1645–1658.

    Article  Google Scholar 

  • Sola A, Corchero C, Salom J, et al. (2018). Simulation tools to build urban-scale energy models: A review. Energies, 11: 3269.

    Article  Google Scholar 

  • Sun Y, Haghighat F, Fung BCM (2020). A review of the-state-of-the-art in data-driven approaches for building energy prediction. Energy and Buildings, 221: 110022.

    Article  Google Scholar 

  • Swan LG, Ugursal VI (2009). Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renewable and Sustainable Energy Reviews, 13: 1819–1835.

    Article  Google Scholar 

  • Tamer T, Gürsel Dino I, Meral Akgül C (2022). Data-driven, long-term prediction of building performance under climate change: building energy demand and BIPV energy generation analysis across Turkey. Renewable and Sustainable Energy Reviews, 162: 112396.

    Article  Google Scholar 

  • Tessmann O, Mehdizadeh S (2019). Rotoform: Realization of hollow construction elements through roto-forming with hyper-elastic membrane formwork. In: Proceedings of the Symposium on Simulation for Architecture and Urban Design, Atlanta, GA, USA.

  • Tian W, Han X, Zuo W, et al. (2018). Building energy simulation coupled with CFD for indoor environment: A critical review and recent applications. Energy and Buildings, 165: 184–199.

    Article  Google Scholar 

  • Ullah N, Siddique MA, Ding M, et al. (2023). The impact of urbanization on urban heat island: Predictive approach using google earth engine and CA-markov modelling (2005–2050) of Tianjin city, China. International Journal of Environmental Research and Public Health, 20: 2642.

    Article  Google Scholar 

  • Vahmani P, Luo X, Jones A, et al. (2022). Anthropogenic heating of the urban environment: An investigation of feedback dynamics between urban micro-climate and decomposed anthropogenic heating from buildings. Building and Environment, 213: 108841.

    Article  Google Scholar 

  • Vallati A, De Lieto Vollaro A, Golasi I, et al. (2015). On the impact of urban micro climate on the energy consumption of buildings. Energy Procedia, 82: 506–511.

    Article  Google Scholar 

  • Van Tol Z, Ellis A (2023). Analysis of urban Heat Island intensity through air mass persistence: A case study of four United States cities. Urban Climate, 47: 101345.

    Article  Google Scholar 

  • Waly NM, Hassan H, Murata R, et al. (2023). Correlating the urban microclimate and energy demands in hot climate Contexts: A hybrid review. Energy and Buildings, 295: 113303.

    Article  Google Scholar 

  • Wang Z, Srinivasan RS (2017). A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renewable and Sustainable Energy Reviews, 75: 796–808.

    Article  Google Scholar 

  • Wang R, Lu S, Li Q (2019). Multi-criteria comprehensive study on predictive algorithm of hourly heating energy consumption for residential buildings. Sustainable Cities and Society, 49: 101623.

    Article  Google Scholar 

  • Wang C, Tindemans S, Miller C, et al. (2020a). Bayesian calibration at the urban scale: a case study on a large residential heating demand application in Amsterdam. Journal of Building Performance Simulation, 13: 347–361.

    Article  Google Scholar 

  • Wang Z, Hong T, Piette MA (2020b). Building thermal load prediction through shallow machine learning and deep learning. Applied Energy, 263: 114683.

    Article  Google Scholar 

  • Wang Z, Hong T, Li H, et al. (2021). Predicting city-scale daily electricity consumption using data-driven models. Advances in Applied Energy, 2: 100025.

    Article  Google Scholar 

  • Wang M, Yu H, Yang Y, et al. (2022a). Assessing the impacts of urban morphology factors on the energy performance for building stocks based on a novel automatic generation framework. Sustainable Cities and Society, 87: 104267.

    Article  Google Scholar 

  • Wang P, Yu P, Lu J, et al. (2022b). The mediation effect of land surface temperature in the relationship between land use-cover change and energy consumption under seasonal variations. Journal of Cleaner Production, 340: 130804.

    Article  Google Scholar 

  • Wei L, Tian W, Zuo J, et al. (2016). Effects of building form on energy use for buildings in cold climate regions. Procedia Engineering, 146: 182–189.

    Article  Google Scholar 

  • Wei Y, Xia L, Pan S, et al. (2019). Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks. Applied Energy, 240: 276–294.

    Article  Google Scholar 

  • Wei X, Guan F, Zhang X, et al. (2023). Integrating planar and vertical environmental features for modelling land surface temperature based on street view images and land cover data. Building and Environment, 235: 110231.

    Article  Google Scholar 

  • Weismayer C, Pezenka I (2017). Identifying emerging research fields: a longitudinal latent semantic keyword analysis. Scientometrics, 113: 1757–1785.

    Article  Google Scholar 

  • Wong CHH, Cai M, Ren C, et al. (2021). Modelling building energy use at urban scale: a review on their account for the urban environment. Building and Environment, 205: 108235.

    Article  Google Scholar 

  • Wu X, Ou J, Wen Y, et al. (2022). Developing a data-fusing method for mapping fine-scale urban three-dimensional building structure. Sustainable Cities and Society, 80: 103716.

    Article  Google Scholar 

  • Xie X, Luo Z, Grimmond S, Sun T, et al. (2022). Impact of inter-building longwave radiative exchanges on building energy performance and indoor overheating. Building and Environment, 209: 108628.

    Article  Google Scholar 

  • Xu L, Tong S, He W, et al. (2022). Better understanding on impact of microclimate information on building energy modelling performance for urban resilience. Sustainable Cities and Society, 80: 103775.

    Article  Google Scholar 

  • Yadav N, Rajendra K, Awasthi A, et al. (2023). Systematic exploration of heat wave impact on mortality and urban heat island: A review from 2000 to 2022. Urban Climate, 51: 101622.

    Article  Google Scholar 

  • Yamaguchi Y, Shimoda Y, Mizuno M (2007). Proposal of a modeling approach considering urban form for evaluation of city level energy management. Energy and Buildings, 39: 580–592.

    Article  Google Scholar 

  • Yang X, Zhao L, Bruse M, et al. (2012). An integrated simulation method for building energy performance assessment in urban environments. Energy and Buildings, 54: 243–251.

    Article  Google Scholar 

  • Yang Y, Javanroodi K, Nik VM (2021). Climate change and energy performance of European residential building stocks–A comprehensive impact assessment using climate big data from the coordinated regional climate downscaling experiment. Applied Energy, 298: 117246.

    Article  Google Scholar 

  • Yang J, Deng Z, Guo S, et al. (2023a). Development of bottom-up model to estimate dynamic carbon emission for city-scale buildings. Applied Energy, 331: 120410.

    Article  Google Scholar 

  • Yang S, Wang LL, Stathopoulos T, et al. (2023b). Urban microclimate and its impact on built environment—A review. Building and Environment, 238: 110334.

    Article  Google Scholar 

  • Yang Y, Gu Q, Wei H, et al. (2023c). Transforming and validating urban microclimate data with multi-sourced microclimate datasets for building energy modelling at urban scale. Energy and Buildings, 295: 113318.

    Article  Google Scholar 

  • Yoo W, Clayton MJ, Yan W (2023). ESMUST: EnergyPlus-driven surrogate model for urban surface temperature prediction. Building and Environment, 229: 109935.

    Article  Google Scholar 

  • Yu J, Chang W, Dong Y (2022). Building energy prediction models and related uncertainties: A review. Buildings, 12: 1284.

    Article  Google Scholar 

  • Yu C, Pan W (2023). Inter-building effect on building energy consumption in high-density city contexts. Energy and Buildings, 278: 112632.

    Article  Google Scholar 

  • Yun GY, Ngarambe J, Duhirwe PN, et al. (2020). Predicting the magnitude and the characteristics of the urban heat island in coastal cities in the proximity of desert landforms. The case of Sydney. The Science of the Total Environment, 709: 136068.

    Article  Google Scholar 

  • Zhan W, Ju W, Hai S, et al. (2014). Satellite-derived subsurface urban heat island. Environmental Science & Technology, 48: 12134–12140.

    Article  Google Scholar 

  • Zhang M, Gao Z (2021). Effect of urban form on microclimate and energy loads: Case study of generic residential district prototypes in Nanjing, China. Sustainable Cities and Society, 70: 102930.

    Article  Google Scholar 

  • Zhang M, Al Kafy A, Xiao P, et al. (2023). Impact of urban expansion on land surface temperature and carbon emissions using machine learning algorithms in Wuhan, China. Urban Climate, 47: 101347.

    Article  Google Scholar 

  • Zhao X, Haberl J (2022). A survey of urban-scale building energy modelers: A report to the ASHRAE Technical Committee TC 4.7, Subcommittee on Multi-Scale Building Energy Modeling (MBEM).

  • Zhong B, Wu H, Li H, et al. (2019). A scientometric analysis and critical review of construction related ontology research. Automation in Construction, 101: 17–31.

    Article  Google Scholar 

  • Zhou D, Zhang L, Li D, et al. (2016). Climate–vegetation control on the diurnal and seasonal variations of surface urban heat islands in China. Environmental Research Letters, 11: 074009.

    Article  Google Scholar 

  • Zhu S, Li Y, Wei S, et al. (2022). The impact of urban vegetation morphology on urban building energy consumption during summer and winter seasons in Nanjing, China. Landscape and Urban Planning, 228: 104576.

    Article  Google Scholar 

  • Zinzi M, Agnoli S, Burattini C, et al. (2020). On the thermal response of buildings under the synergic effect of heat waves and urban heat island. Solar Energy, 211: 1270–1282.

    Article  Google Scholar 

  • Zonato A, Martilli A, Gutierrez E, et al. (2021). Exploring the effects of rooftop mitigation strategies on urban temperatures and energy consumption. Journal of Geophysical Research: Atmospheres, 126: e2021JD035002.

    Article  Google Scholar 

  • Zurqani HA, Post CJ, Mikhailova EA, et al. (2020). Evaluating the integrity of forested riparian buffers over a large area using LiDAR data and Google Earth Engine. Scientific Reports, 10: 14096.

    Article  Google Scholar 

Download references

Acknowledgements

The resources utilized for this study are supported through the Sponsored Research and Industrial Consultancy (SRIC) grant No: IIT/SRIC/AR/MWS/2021-2022/057, and the SERB grant No. IPA/2021/000081.

Author information

Authors and Affiliations

  1. Department of Architecture and Regional Planning, Indian Institute of Technology Kharagpur, Kharagpur, India

    Nilabhra Mondal & Prashant Anand

  2. Department of Geography, Lalbaba College, University of Calcutta, Kolkata, India

    Ansar Khan

  3. School of Architecture, Design and Planning, The University of Sydney, Sydney, Australia

    Chirag Deb

  4. Department of the Built Environment, National University of Singapore, Singapore, Singapore

    David Cheong & Chandra Sekhar

  5. Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, USA

    Dev Niyogi

  6. Department of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin, Austin, USA

    Dev Niyogi

  7. Faculty of Built Environment, University of New South Wales, Kensington, Australia

    Mattheos Santamouris

Authors
  1. Nilabhra Mondal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prashant Anand
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ansar Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chirag Deb
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David Cheong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chandra Sekhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dev Niyogi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mattheos Santamouris
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Nilabhra Mondal: conceptualization, writing, analysis and editing original manuscript; Prashant Anand: conceptualization, supervision, writing and editing; Ansar Khan: supervision, review and editing; Chirag Deb: supervision, review and editing; David Cheong: supervision, writing, review and editing; Chandra Sekhar: supervision, writing, review and editing; Dev Niyogi: supervision, review and editing; Mattheos Santamouris: supervision, review and editing.

Corresponding author

Correspondence to Prashant Anand.

Ethics declarations

The authors have no competing interests to declare that are relevant to the content of this article. Mattheos Santamouris is an Editorial Board member of Building Simulation.

Appendix to

12273_2024_1112_MOESM1_ESM.pdf

Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities: Current trends and future outlook

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mondal, N., Anand, P., Khan, A. et al. Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities: Current trends and future outlook. Build. Simul. 17, 695–722 (2024). https://doi.org/10.1007/s12273-024-1112-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12273-024-1112-y

Keywords

Search

Navigation