IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v112y2019icp461-470.html
   My bibliography  Save this article

Analysing the impact of spatial context on the heat consumption of individual households

Author

Listed:
  • Rafiee, A.
  • Dias, E.
  • Koomen, E.

Abstract

The heating of houses comprises a considerable share of the total energy consumption in many developed countries in temperate and colder climates. While most of the factors affecting space heating depend on individual choices (e.g. occupants’ behaviour, interior building design, heating system efficiency) that are difficult to influence through urban planning, spatial context of individual housing units is within the sphere of influence of planners. Yet the impact of spatial context has hitherto received limited research attention due to the lack of geospatial data and the massive computer processing required to capture the shape and surroundings of individual housing units.

Suggested Citation

  • Rafiee, A. & Dias, E. & Koomen, E., 2019. "Analysing the impact of spatial context on the heat consumption of individual households," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 461-470.
    • Handle: RePEc:eee:rensus:v:112:y:2019:i:c:p:461-470
    DOI: 10.1016/j.rser.2019.05.033
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032119303466
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2019.05.033?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Say, Nuriye Peker & Yucel, Muzaffer, 2006. "Energy consumption and CO2 emissions in Turkey: Empirical analysis and future projection based on an economic growth," Energy Policy, Elsevier, vol. 34(18), pages 3870-3876, December.
    2. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    3. De Boeck, L. & Verbeke, S. & Audenaert, A. & De Mesmaeker, L., 2015. "Improving the energy performance of residential buildings: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 960-975.
    4. J. Daniel Khazzoom, 1987. "Energy Saving Resulting from the Adoption of More Efficient Appliances," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 85-89.
    5. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    6. Sanaieian, Haniyeh & Tenpierik, Martin & Linden, Kees van den & Mehdizadeh Seraj, Fatemeh & Mofidi Shemrani, Seyed Majid, 2014. "Review of the impact of urban block form on thermal performance, solar access and ventilation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 551-560.
    7. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    8. Pacheco, R. & Ordóñez, J. & Martínez, G., 2012. "Energy efficient design of building: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3559-3573.
    9. Eric Koomen & Piet Rietveld & Fernando Bacao, 2009. "The Third Dimension in Urban Geography: The Urban-Volume Approach," Environment and Planning B, , vol. 36(6), pages 1008-1025, December.
    10. Chalal, Moulay Larbi & Benachir, Medjdoub & White, Michael & Shrahily, Raid, 2016. "Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 761-776.
    11. Frayssinet, Loïc & Merlier, Lucie & Kuznik, Frédéric & Hubert, Jean-Luc & Milliez, Maya & Roux, Jean-Jacques, 2018. "Modeling the heating and cooling energy demand of urban buildings at city scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2318-2327.
    12. Yamasaki, Eiji & Tominaga, Norio, 1997. "Evolution of an aging society and effect on residential energy demand," Energy Policy, Elsevier, vol. 25(11), pages 903-912, September.
    13. Rode, Philipp & Keim, Christian & Robazza, Guido & Viejo, Pablo & Schofield, James, 2014. "Cities and energy: urban morphology and residential heat-energy demand," LSE Research Online Documents on Economics 60778, London School of Economics and Political Science, LSE Library.
    14. Wang, Zeyu & Srinivasan, Ravi S., 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, Elsevier, vol. 75(C), pages 796-808.
    15. Philipp Rode & Christian Keim & Guido Robazza & Pablo Viejo & James Schofield, 2014. "Cities and Energy: Urban Morphology and Residential Heat-Energy Demand," Environment and Planning B, , vol. 41(1), pages 138-162, February.
    16. Berkhout, Peter H. G. & Muskens, Jos C. & W. Velthuijsen, Jan, 2000. "Defining the rebound effect," Energy Policy, Elsevier, vol. 28(6-7), pages 425-432, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ehsan Ahmadian & Chris Bingham & Amira Elnokaly & Behzad Sodagar & Ivan Verhaert, 2022. "Impact of Climate Change and Technological Innovation on the Energy Performance and Built form of Future Cities," Energies, MDPI, vol. 15(22), pages 1-22, November.
    2. Marvuglia, Antonino & Havinga, Lisanne & Heidrich, Oliver & Fonseca, Jimeno & Gaitani, Niki & Reckien, Diana, 2020. "Advances and challenges in assessing urban sustainability: an advanced bibliometric review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    2. Toparlar, Y. & Blocken, B. & Maiheu, B. & van Heijst, G.J.F., 2018. "Impact of urban microclimate on summertime building cooling demand: A parametric analysis for Antwerp, Belgium," Applied Energy, Elsevier, vol. 228(C), pages 852-872.
    3. Tomasz Szul & Stanisław Kokoszka, 2020. "Application of Rough Set Theory (RST) to Forecast Energy Consumption in Buildings Undergoing Thermal Modernization," Energies, MDPI, vol. 13(6), pages 1-17, March.
    4. Ding, Zhikun & Chen, Weilin & Hu, Ting & Xu, Xiaoxiao, 2021. "Evolutionary double attention-based long short-term memory model for building energy prediction: Case study of a green building," Applied Energy, Elsevier, vol. 288(C).
    5. Zhang, Ji & Xu, Le & Shabunko, Veronika & Tay, Stephen En Rong & Sun, Huixuan & Lau, Stephen Siu Yu & Reindl, Thomas, 2019. "Impact of urban block typology on building solar potential and energy use efficiency in tropical high-density city," Applied Energy, Elsevier, vol. 240(C), pages 513-533.
    6. Khamma, Thulasi Ram & Zhang, Yuming & Guerrier, Stéphane & Boubekri, Mohamed, 2020. "Generalized additive models: An efficient method for short-term energy prediction in office buildings," Energy, Elsevier, vol. 213(C).
    7. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    8. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).
    9. Gatt, Damien & Yousif, Charles & Cellura, Maurizio & Camilleri, Liberato & Guarino, Francesco, 2020. "Assessment of building energy modelling studies to meet the requirements of the new Energy Performance of Buildings Directive," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    10. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
    11. Shen, Yuxuan & Pan, Yue, 2023. "BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization," Applied Energy, Elsevier, vol. 333(C).
    12. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    13. Teresa Santos & Raquel Deus & Jorge Rocha & José António Tenedório, 2021. "Assessing Sustainable Urban Development Trends in a Dynamic Tourist Coastal Area Using 3D Spatial Indicators," Energies, MDPI, vol. 14(16), pages 1-22, August.
    14. D'Amico, A. & Ciulla, G. & Panno, D. & Ferrari, S., 2019. "Building energy demand assessment through heating degree days: The importance of a climatic dataset," Applied Energy, Elsevier, vol. 242(C), pages 1285-1306.
    15. repec:hal:gemwpa:hal-00991732 is not listed on IDEAS
    16. Tomasz Szul & Krzysztof Nęcka & Stanisław Lis, 2021. "Application of the Takagi-Sugeno Fuzzy Modeling to Forecast Energy Efficiency in Real Buildings Undergoing Thermal Improvement," Energies, MDPI, vol. 14(7), pages 1-16, March.
    17. Sun, Xiaoqi & Liu, Xiaojia, 2020. "Decomposition analysis of debt’s impact on China’s energy consumption," Energy Policy, Elsevier, vol. 146(C).
    18. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    19. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
    20. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    21. Freire-González, Jaume, 2017. "Evidence of direct and indirect rebound effect in households in EU-27 countries," Energy Policy, Elsevier, vol. 102(C), pages 270-276.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:112:y:2019:i:c:p:461-470. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.