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Predicting the diversity of internal temperatures from the English residential sector using panel methods

Author

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  • Kelly, Scott
  • Shipworth, Michelle
  • Shipworth, David
  • Gentry, Michael
  • Wright, Andrew
  • Pollitt, Michael
  • Crawford-Brown, Doug
  • Lomas, Kevin

Abstract

In this paper, panel methods are applied in new and innovative ways to predict daily mean internal temperature demand across a heterogeneous domestic building stock over time. This research not only exploits a rich new dataset but presents new methodological insights and offers important linkages for connecting bottom-up building stock models to human behaviour. It represents the first time a panel model has been used to estimate the dynamics of internal temperature demand from the natural daily fluctuations of external temperature combined with important behavioural, socio-demographic and building efficiency variables. The model is able to predict internal temperatures across a heterogeneous building stock to within ∼0.71°C at 95% confidence and explain 45% of the variance of internal temperature between dwellings. The model confirms hypothesis from sociology and psychology that habitual behaviours are important drivers of home energy consumption. In addition, the model offers the possibility to quantify take-back (direct rebound effect) owing to increased internal temperatures from the installation of energy efficiency measures. The presence of thermostats or thermostatic radiator valves (TRVs) are shown to reduce average internal temperatures, however, the use of an automatic timer is shown to be statistically insignificant. The number of occupants, household income and occupant age are all important factors that explain a quantifiable increase in internal temperature demand. Households with children or retired occupants are shown to have higher average internal temperatures than households who do not. As expected, building typology, building age, roof insulation thickness, wall U-value and the proportion of double glazing all have positive and statistically significant effects on daily mean internal temperature. In summary, the model can be either used to make statistical inferences about the importance of different factors for explaining internal temperatures or as a predictive tool. However, a key contribution of this research is the possibility to use this model to calibrate existing building stock for behaviour and socio-demographic effects leading to improved estimations of domestic energy demand.

Suggested Citation

  • Kelly, Scott & Shipworth, Michelle & Shipworth, David & Gentry, Michael & Wright, Andrew & Pollitt, Michael & Crawford-Brown, Doug & Lomas, Kevin, 2013. "Predicting the diversity of internal temperatures from the English residential sector using panel methods," Applied Energy, Elsevier, vol. 102(C), pages 601-621.
    • Handle: RePEc:eee:appene:v:102:y:2013:i:c:p:601-621
    DOI: 10.1016/j.apenergy.2012.08.015
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    5. Lomas, K.J. & Oliveira, S. & Warren, P. & Haines, V.J. & Chatterton, T. & Beizaee, A. & Prestwood, E. & Gething, B., 2018. "Do domestic heating controls save energy? A review of the evidence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 52-75.
    6. Dodds, Paul E., 2014. "Integrating housing stock and energy system models as a strategy to improve heat decarbonisation assessments," Applied Energy, Elsevier, vol. 132(C), pages 358-369.
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    9. Hanli Chen & Chunmei Lu, 2023. "Research on the Spatial Effect and Threshold Characteristics of New-Type Urbanization on Carbon Emissions in China’s Construction Industry," Sustainability, MDPI, vol. 15(22), pages 1-26, November.
    10. Namazkhan, Maliheh & Albers, Casper & Steg, Linda, 2019. "The role of environmental values, socio-demographics and building characteristics in setting room temperatures in winter," Energy, Elsevier, vol. 171(C), pages 1183-1192.
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    13. Renaldi, R. & Kiprakis, A. & Friedrich, D., 2017. "An optimisation framework for thermal energy storage integration in a residential heat pump heating system," Applied Energy, Elsevier, vol. 186(P3), pages 520-529.
    14. Radhi, Hassan & Sharples, Stephen, 2013. "Quantifying the domestic electricity consumption for air-conditioning due to urban heat islands in hot arid regions," Applied Energy, Elsevier, vol. 112(C), pages 371-380.
    15. Karol Bandurski & Andrzej Górka & Halina Koczyk, 2023. "Radiators Adjustment in Multi-Family Residential Buildings—An Analysis Based on Data from Heat Meters," Energies, MDPI, vol. 16(22), pages 1-22, November.
    16. Lu, Heli & Liu, Guifang, 2014. "Spatial effects of carbon dioxide emissions from residential energy consumption: A county-level study using enhanced nocturnal lighting," Applied Energy, Elsevier, vol. 131(C), pages 297-306.
    17. Yin, Peng & Xie, Jingchao & Ji, Ying & Liu, Jiaping & Hou, Qixian & Zhao, Shanshan & Jing, Pengfei, 2023. "Winter indoor thermal environment and heating demand of low-quality centrally heated houses in cold climates," Applied Energy, Elsevier, vol. 331(C).
    18. Andersen, Kristoffer Steen & Wiese, Catharina & Petrovic, Stefan & McKenna, Russell, 2020. "Exploring the role of households’ hurdle rates and demand elasticities in meeting Danish energy-savings target," Energy Policy, Elsevier, vol. 146(C).
    19. Yohan Kim & Scott Kelly & Deepu Krishnan & Jay Falletta & Kerryn Wilmot, 2022. "Strategies for Imputation of High-Resolution Environmental Data in Clinical Randomized Controlled Trials," IJERPH, MDPI, vol. 19(3), pages 1-17, January.
    20. Ardeshir Mahdavi & Christiane Berger & Hadeer Amin & Eleni Ampatzi & Rune Korsholm Andersen & Elie Azar & Verena M. Barthelmes & Matteo Favero & Jakob Hahn & Dolaana Khovalyg & Henrik N. Knudsen & Ale, 2021. "The Role of Occupants in Buildings’ Energy Performance Gap: Myth or Reality?," Sustainability, MDPI, vol. 13(6), pages 1-44, March.
    21. José Joaquín Aguilera & Rune Korsholm Andersen & Jørn Toftum, 2019. "Prediction of Indoor Air Temperature Using Weather Data and Simple Building Descriptors," IJERPH, MDPI, vol. 16(22), pages 1-20, November.
    22. Huang, Luling & Nock, Destenie, 2024. "Estimating the income-related inequality aversion to energy limiting behavior in the United States," Energy Economics, Elsevier, vol. 136(C).
    23. Eyre, Nick & Baruah, Pranab, 2015. "Uncertainties in future energy demand in UK residential heating," Energy Policy, Elsevier, vol. 87(C), pages 641-653.
    24. Wang, Shaojian & Fang, Chuanglin & Guan, Xingliang & Pang, Bo & Ma, Haitao, 2014. "Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces," Applied Energy, Elsevier, vol. 136(C), pages 738-749.

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