Skip to main content
SpringerLink
Log in

A review of operational energy consumption calculation method for urban buildings

  • Review Article
  • Building Thermal, Lighting, and Acoustics Modeling
  • Published:
Building Simulation Aims and scope Submit manuscript

Abstract

Rapid urbanization has driven economic and social development, but it has also led to continued growth in building energy consumption. It is of great significance to ensure the user comfort while controlling the growth of building energy use. Accurate quantification of urban buildings' energy demand can support energy efficient and sustainable community design, assist urban morphology generation and optimization, building layout optimization, building shape and construction design, HVAC system optimization, assessment of the energy program and policy. In recent years, researchers worldwide have carried out research of urban scale energy consumption calculation methods from different perspectives, and encountered different technical difficulties. This paper provides a critical review on the energy modeling methods at urban neighborhood scale from the following three aspects: database, models and platforms. Through literature review, the authors indicate the advantages and limitations of current urban building energy calculation methods and tools, and propose the following possible approaches to improve the operational energy consumption calculation method for urban buildings: (1) develop micro-environment data generation methods that can be directly applied to energy consumption calculation of urban buildings; (2) improve the capabilities to collect, filter and convert the building information data by introducing the data mining technique; (3) introduce the cluster analysis and artificial intelligence technology to improve the speed of energy consumption calculation; (4) develop a visualization platform to realize real-time editing and calculating of urban design.

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.

Similar content being viewed by others

References

  • Allegrini J, Dorer V, Carmeliet J (2012). Influence of the urban microclimate in street canyons on the energy demand for space cooling and heating of buildings. Energy and Buildings, 55: 823–832.

    Google Scholar 

  • Arayici Y (2007). An approach for real world data modelling with the 3D terrestrial laser scanner for built environment. Automation in Construction, 16: 816–829.

    Google Scholar 

  • Ascione F, De Masi RF, de Rossi F, Fistola R, Sasso M, Vanoli GP (2013). Analysis and diagnosis of the energy performance of buildings and districts: Methodology, validation and development of Urban Energy Maps. Cities, 35: 270–283.

    Google Scholar 

  • Bahu JM, Koch A, Kremers E, Murshed SM (2014). Towards a 3D spatial urban energy modelling approach. International Journal of 3-D Information Modeling, 3: 1–16.

    Google Scholar 

  • Bueno B, Norford L, Pigeon G, Britter R (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.

    Google Scholar 

  • Bueno B, Roth M, Norford L, Li R (2014). Computationally efficient prediction of canopy level urban air temperature at the neighbourhood scale. Urban Climate, 9: 35–53.

    Google Scholar 

  • Busch RD (1996). Method of energy analysis. In: Hunn BD (ed), Fundamentals of Building Energy Dynamics. Cambridge, USA: MIT Press. pp. 220–237.

    Google Scholar 

  • Calcerano F, Martinelli L (2016). Numerical optimisation through dynamic simulation of the position of trees around a stand-alone building to reduce cooling energy consumption. Energy and Buildings, 112: 234–243.

    Google Scholar 

  • Caputo P, Costa G, Ferrari S (2013). A supporting method for defining energy strategies in the building sector at urban scale. Energy Policy, 55: 261–270.

    Google Scholar 

  • Chen Y, Hong T, Piette MA (2017a). Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis. Applied Energy, 205: 323–335.

    Google Scholar 

  • Chen Y, Hong T, Piette MA (2017b). City-scale building retrofit analysis: A case study using CityBES. In: Proceedings of International IBPSA Building Simulation conference, San Francisco, USA.

    Google Scholar 

  • Chen Y, Hong T, Luo X, Hooper B (2019). Development of city buildings dataset for urban building energy modeling. Energy and Buildings, 183: 252–265.

    Google Scholar 

  • Chun B, Guldmann JM (2014). Spatial statistical analysis and simulation of the urban heat island in high-density central cities. Landscape and Urban Planning, 125: 76–88.

    Google Scholar 

  • Corrado V, Ballarini I, Madrazo L, Nemirovskij G (2015). Data structuring for the ontological modelling of urban energy systems: The experience of the SEMANCO project. Sustainable Cities and Society, 14: 223–235.

    Google Scholar 

  • Dascalaki EG, Droutsa K, Gaglia AG, Kontoyiannidis S, Balaras CA (2010). Data collection and analysis of the building stock and its energy performance—An example for Hellenic buildings. Energy and Buildings, 42: 1231–1237.

    Google Scholar 

  • Dascalaki EG, Droutsa KG, Balaras CA, Kontoyiannidis S (2011). Building typologies as a tool for assessing the energy performance of residential buildings—A case study for the Hellenic building stock. Energy and Buildings, 43: 3400–3409.

    Google Scholar 

  • Davila CC, 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.

    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.

    Google Scholar 

  • Du S, Zhang F, Zhang X (2015). Semantic classification of urban buildings combining VHR image and GIS data: an improved random forest approach. ISPRS Journal of Photogrammetry and Remote Sensing, 105: 107–119.

    Google Scholar 

  • Eicker U, Zirak M, Bartke N, Rodríguez LR, Coors V (2018). New 3D model based urban energy simulation for climate protection concepts. Energy and Buildings, 163: 79–91.

    Google Scholar 

  • Fabbri K, Zuppiroli M, Ambrogio K (2012). Heritage buildings and energy performance: Mapping with GIS tools. Energy and Buildings, 48: 137–145.

    Google Scholar 

  • Famuyibo AA, Duffy A, Strachan P (2012). Developing archetypes for domestic dwellings—An Irish case study. Energy and Buildings, 50: 150–157.

    Google Scholar 

  • Fan H, MacGill IF, Sproul AB (2015). Statistical analysis of driving factors of residential energy demand in the greater Sydney region, Australia. Energy and Buildings, 105: 9–25.

    Google Scholar 

  • Farahbakhsh H, Ugursal VI, Fung AS (1998). A residential end-use energy consumption model for Canada. International Journal of Energy Research, 22: 1133–1143.

    Google Scholar 

  • Filogamo L, Peri G, Rizzo G, Giaccone A (2014). On the classification of large residential buildings stocks by sample typologies for energy planning purposes. Applied Energy, 135: 825–835.

    Google Scholar 

  • Fischer RD, Flanigan LJ, Talbert SG (1982). Degree day method for simplified energy analysis. ASHRAE Transactions, 88(2): 522–571.

    Google Scholar 

  • Fonseca JA, Nguyen TA, Schlueter A, Marechal F (2016). City Energy Analyst (CEA): Integrated framework for analysis and optimization of building energy systems in neighborhoods and City districts. Energy and Buildings, 113: 202–226.

    Google Scholar 

  • Gago EJ, Roldan J, Pacheco-Torres R, Ordóñez J (2013). The city and urban heat islands: A review of strategies to mitigate adverse effects. Renewable and Sustainable Energy Reviews, 25: 749–758.

    Google Scholar 

  • Gimenez L, Hippolyte JL, Robert S, Suard F, Zreik K (2015). Review: reconstruction of 3D building information models from 2D scanned plans. Journal of Building Engineering, 2: 24–35.

    Google Scholar 

  • Gracik S, Heidarinejad M, Liu J, Srebric J (2015). Effect of urban neighborhoods on the performance of building cooling systems. Building and Environment, 90: 15–29.

    Google Scholar 

  • Guattari C, Evangelisti L, Balaras CA (2018). On the assessment of urban heat island phenomenon and its effects on building energy performance: A case study of Rome (Italy). Energy and Buildings, 158: 605–615.

    Google Scholar 

  • Gudnason G, Katranuschkov P, Scherer RJ, Balaras CA (2014). Framework for sharing and re-use of domain data in whole building energy simulation. In: Proceedings of the 10th European Conference on Product & Process Modelling, Vienna, Austria.

    Google Scholar 

  • Haala N, Peter M, Cefalu A, Kremer J (2008). Mobile LiDAR mapping for urban data capture. In: Proceedings of the 14th International Conference on Virtual Systems and Multimedia, Limassol, Cyprus.

    Google Scholar 

  • Hens H, Verbeeck G, Verdonck B (2001). Impact of energy efficiency measures on the CO2 emissions in the residential sector, a large scale analysis. Energy and Buildings, 33: 275–281.

    Google Scholar 

  • Hong T, Chen Y, Lee SH, Piette MA (2016). CityBES: A web-based platform to support city-scale building energy efficiency. In: Proceedings of the 5th International Urban Computing Workshop, San Francisco, USA.

    Google Scholar 

  • IEA (2008). World energy outlook. International Energy Agency.

    Google Scholar 

  • ISO (2008). ISO 13790. Energy Performance of Buildings—Calculation of Energy Use for Space Heating and Cooling.

    Google Scholar 

  • Kavgic M, Mavrogianni A, Mumovic D, Summerfield A, Stevanovic Z, Djurovic-Petrovic M (2010). A review of bottom-up building stock models for energy consumption in the residential sector. Building and Environment, 45: 1683–1697.

    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.

    Google Scholar 

  • Ko Y (2013). Urban form and residential energy use: a review of design principles and empirical findings. Journal of Planning Literature, 28: 327–351.

    Google Scholar 

  • Kontokosta CE, Tull C (2017). A data-driven predictive model of city-scale energy use in buildings. Applied Energy, 197: 303–317.

    Google Scholar 

  • Kristensen MH, Hedegaard RE, Petersen S (2018). Hierarchical calibration of archetypes for urban building energy modeling. Energy and Buildings, 175: 219–234.

    Google Scholar 

  • Lafarge F, Mallet C (2012). Creating large-scale city models from 3D-point clouds: a robust approach with hybrid representation. International Journal of Computer Vision, 99: 69–85.

    MathSciNet  Google Scholar 

  • Li Q, Quan SJ, Augenbroe G, Yang PP, Brown J (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.

    Google Scholar 

  • Li W, Zhou Y, Cetin K, Eom J, Wang Y, Chen G, Zhang X (2017). Modeling urban building energy use: A review of modeling approaches and procedures. Energy, 141: 2445–2457.

    Google Scholar 

  • Li X, Yao R, Liu M, Costanzo V, Yu W, Wang W, Short A, Li B (2018). Developing urban residential reference buildings using clustering analysis of satellite images. Energy and Buildings, 169: 417–429.

    Google Scholar 

  • Liu D, Liu J, Yang L (2013). Review of building energy consumption calculation. Journal of HV&AC, 2013, 43(1): 95–99. (in Chinese)

    MathSciNet  Google Scholar 

  • Ma J, Cheng JCP (2016). Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology. Applied Energy, 183: 182–192.

    Google Scholar 

  • Ma W, Fang S, Liu G, Zhou R (2017). Modeling of district load forecasting for distributed energy system. Applied Energy, 204: 181–205.

    Google Scholar 

  • Madrazo L, Nemirovski G, Sicilia Á (2013). Shared vocabularies to support the creation of energy urban systems models. In: Proceedings of the 4th workshop organised by the EEB data models community ICT for sustainable places Nice, France.

    Google Scholar 

  • Martin M, Afshari A, Armstrong PR, Norford LK (2015). Estimation of urban temperature and humidity using a lumped parameter model coupled with an EnergyPlus model. Energy and Buildings, 96: 221–235.

    Google Scholar 

  • Mata É, Kalagasidis AS, Johnsson F (2014). Building-stock aggregation through archetype buildings: France, Germany, Spain and the UK. Building and Environment, 81: 270–282.

    Google Scholar 

  • Mattinen MK, Heljo J, Vihola J, Kurvinen A, Lehtoranta S, Nissinen A (2014). Modeling and visualization of residential sector energy consumption and greenhouse gas emissions. Journal of Cleaner Production, 81: 70–80.

    Google Scholar 

  • Meinel G, Hecht R, Herold H (2009). Analyzing building stock using topographic maps and GIS. Building Research & Information, 37: 468–482.

    Google Scholar 

  • Moghadam ST, Toniolo J, Mutani G, Lombardi P (2018). A GIS-statistical approach for assessing built environment energy use at urban scale. Sustainable Cities and Society, 37: 70–84.

    Google Scholar 

  • Monteiro CS, Costa C, Pina A, Santos MY, Ferrão P (2018). An urban building database (UBD) supporting a smart city information system. Energy and Buildings, 158: 244–260.

    Google Scholar 

  • Mukherjee S, Nateghi R (2017). Climate sensitivity of end-use electricity consumption in the built environment: an application to the state of Florida, United States. Energy, 128: 688–700.

    Google Scholar 

  • Nall DH, Arens EA (1979). Influence of degree day base temperature on residential building energy prediction. ASHRAE Transactions, 85(1): 727–735.

    Google Scholar 

  • Nemirovski G, Nolle A, Sicilia Á, Ballarini I, Corado V (2013). Data integration driven ontology design, case study smart city. In: Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics, Madrid, Spain.

    Google Scholar 

  • Nemry F, Uihlein A, Colodel CM, Wetzel C, Braune A, et al. (2010). Options to reduce the environmental impacts of residential buildings in the European Union—Potential and costs. Energy and Buildings, 42: 976–984.

    Google Scholar 

  • New J, Adams M, Im P, Yang HL, Hambrick J, Copeland W, Bruce L, Ingraham JA (2018). Automatic building energy model creation (AutoBEM) for urban-scale energy modeling and assessment of value propositions for electric utilities. In: Proceedings of the International Conference on Energy Engineering and Smart Grids.

    Google Scholar 

  • Nolle A, Nemirovskij G (2013). ELITE: An entailment-based federated query engine for complete and transparent semantic data integration. In: Proceedings of the International Workshop on Description Logic Ulm, Germany.

    Google Scholar 

  • Nouvel R, Zirak M, Dastageeri H, Coors V, Eicker U (2014). Urban energy analysis based on 3D city model for national scale applications. In: Proceedings of IBPSA Germany Conference, Aachen, Germany.

    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.

    Google Scholar 

  • Oh SJ, Ng KC, Thu K, Chun W, Chua KJE (2016). Forecasting long-term electricity demand for cooling of Singapore’s buildings incorporating an innovative air-conditioning technology. Energy and Buildings, 127: 183–193.

    Google Scholar 

  • Parekh A (2005). Development of archetypes of building characteristics libraries for simplified energy use evaluation of houses. In Proceedings of the 9th International IBPSA Building Simulation Conference, Montréal, Canada.

    Google Scholar 

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

    Google Scholar 

  • Raftery P, Keane M, O’Donnell J (2011). Calibrating whole building energy models: an evidence-based methodology. Energy and Buildings, 43: 2356–2364.

    Google Scholar 

  • Reinhart CF, Dogan T, Jakubiec JA, Rakha T, Sang A (2013). UMI—An urban simulation environment for building energy use, daylighting and walkability. In: Proceedings of the 13th International IBPSA Building Simulation Conference, Chambéry, France.

    Google Scholar 

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

    Google Scholar 

  • Sahin C, Alkis A, Ergun B, Kulur S, Batuk F, Kilic A (2012). Producing 3D city model with the combined photogrammetric and laser scanner data in the example of TaksimCumhuriyet square. Optics and Lasers in Engineering, 50: 1844–1853.

    Google Scholar 

  • Salata F, Golasi I, de LietoVollaro A, de LietoVollaro R (2015). How high albedo and traditional buildings’ materials and vegetation affect the quality of urban microclimate. A case study. Energy and Buildings, 99: 32–49.

    Google Scholar 

  • Sartori I, Wachenfeldt BJ, Hestnes AG (2009). Energy demand in the Norwegian building stock: Scenarios on potential reduction. Energy Policy, 37: 1614–1627.

    Google Scholar 

  • Shi Z, Fonseca JA, Schlueter A (2017). A review of simulation-based urban form generation and optimization for energy-driven urban design. Building and Environment, 121: 119–129.

    Google Scholar 

  • Skelhorn CP, Levermore G, Lindley SJ (2016). Impacts on cooling energy consumption due to the UHI and vegetation changes in Manchester, UK. Energy and Buildings, 122: 150–159.

    Google Scholar 

  • Sofic M, Korjenic A, Bednar T (2011). Quantification of safety factors for simplified heating and cooling demand calculation methods for Vienna. Building Simulation, 4: 189–204.

    Google Scholar 

  • Sokol J, Cerezo Davila C, Reinhart CF (2017). Validation of a Bayesian-based method for defining residential archetypes in urban building energy models. Energy and Buildings, 134: 11–24.

    Google Scholar 

  • Sovacool BK, Brown MA (2010). Twelve metropolitan carbon footprints: a preliminary comparative global assessment. Energy Policy, 38: 4856–4869.

    Google Scholar 

  • Stamper MB (1979). Annual cycle moisture analysis. In: Proceedings of the Conference of the Thermal Performance of the Exterior Envelopes of Buildings, Kissimmee, FL, USA.

    Google Scholar 

  • Strømann-Andersen JB (2012). Integrated energy design in master planning. PhD Thesis, Technical University of Denmark.

    Google Scholar 

  • Sun Y, Heo Y, Tan MHY, Xie H, Wu CFJ, Augenbroe G (2014). Uncertainty quantification of microclimate variables in building energy models. Journal of Building Performance Simulation, 7: 17–32.

    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.

    Google Scholar 

  • Theodoridou I, Papadopoulos AM, Hegger M (2011). A typological classification of the Greek residential building stock. Energy and Buildings, 43: 2779–2787.

    Google Scholar 

  • Truong-Hong L, Laefer DF (2015). Quantitative evaluation strategies for urban 3D model generation from remote sensing data. Computers & Graphics, 49: 82–91.

    Google Scholar 

  • Tsoka S, Tolika K, Theodosiou T, Tsikaloudaki K, Bikas D (2018). A method to account for the urban microclimate on the creation of ‘typical weather year’ datasets for building energy simulation, using stochastically generated data. Energy and Buildings, 165: 270–283.

    Google Scholar 

  • Uihlein A, Eder P (2010). Policy options towards an energy efficient residential building stock in the EU-27. Energy and Buildings, 42: 791–798.

    Google Scholar 

  • Vartholomaios A (2017). A parametric sensitivity analysis of the influence of urban form on domestic energy consumption for heating and cooling in a Mediterranean city. Sustainable Cities and Society, 28: 135–145.

    Google Scholar 

  • Van der Meer WJ (1978). Energy conservative housing for New Mexico. Albuquerque, NM: University of New Mexico.

    Google Scholar 

  • Wang D, Landolt J, Mavromatidis G, Orehounig K, Carmeliet J (2018). CESAR: A bottom-up building stock modelling tool for Switzerland to address sustainable energy transformation strategies. Energy and Buildings, 169: 9–26.

    Google Scholar 

  • Xu X, Wang W, Hong T, Chen J (2019). Incorporating machine learning with building network analysis to predict multi-building energy use. Energy and Buildings, 186: 80–97.

    Google Scholar 

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

    Google Scholar 

  • Yu D (2018). A two-step approach to forecasting city-wide building energy demand. Energy and Buildings, 160: 1–9.

    Google Scholar 

  • Zekar A, Khatib SE (2018). Development and assessment of simplified building representations under the context of an urban energy model: Application to arid climate environment. Energy and Buildings, 173: 461–469.

    Google Scholar 

  • Zhang R, Mirzaei PA, Jones B (2018). Development of a dynamic external CFD and BES coupling framework for application of urban neighbourhoods energy modelling. Building and Environment, 146: 37–49

    Google Scholar 

Download references

Acknowledgements

This research is supported by the China Postdoctoral Science Foundation funded project (No. 2019M650408), and the National Key Research and Development Project (2019YFE010332)

Author information

Authors and Affiliations

  1. College of Architecture & Urban Planning, Beijing University of Technology, Beijing, 100124, China

    Ziwei Li, Shanwen Zheng & Jian Dai

  2. Department of Building Science, Tsinghua University, Beijing, 100084, China

    Borong Lin

  3. College of Civil Engineering, Guangzhou University, Guangzhou, 510006, China

    Yanchen Liu

  4. Building Technology and Urban Systems Division, Lawrence Berkeley National Laboratory, Berkeley, USA

    Zhe Wang

Authors
  1. Ziwei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Borong Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shanwen Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yanchen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhe Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jian Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jian Dai.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Z., Lin, B., Zheng, S. et al. A review of operational energy consumption calculation method for urban buildings. Build. Simul. 13, 739–751 (2020). https://doi.org/10.1007/s12273-020-0619-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12273-020-0619-0

Keywords

Search

Navigation