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The impact of weather variation on energy consumption in residential houses

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  • Fikru, Mahelet G.
  • Gautier, Luis

Abstract

This paper studies the impact of weather variation on energy use by using 5-minutes interval weather–energy data obtained from two residential houses: house 1 is a conventional house with advanced efficiency features and house 2 is a net-zero solar house with relatively more advanced efficiency features. Our result suggests that energy consumption in house 2 is not as sensitive to changes in weather variables as the conventional house. On average, we find that a one unit increase in heating and cooling degree minutes increases energy use by about 9% and 5% respectively for house 1 and 5% and 4% respectively for house 2. In addition, our findings suggest that non-temperature variables such as solar radiation and humidity affect energy use where the sensitivity rates for house 2 are consistently lower than that of house 1. Furthermore our result suggests that the sensitivity of energy use to weather depends on the season and specific time of the day/night.

Suggested Citation

  • Fikru, Mahelet G. & Gautier, Luis, 2015. "The impact of weather variation on energy consumption in residential houses," Applied Energy, Elsevier, vol. 144(C), pages 19-30.
    • Handle: RePEc:eee:appene:v:144:y:2015:i:c:p:19-30
    DOI: 10.1016/j.apenergy.2015.01.040
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    1. Lee, Gi-Eu, 2016. "Temperature Effects are more Complex than Degrees: A Case Study on Residential Energy Consumption," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 242285, Agricultural and Applied Economics Association.
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    4. Yongxia Ding & Wei Qu & Shuwen Niu & Man Liang & Wenli Qiang & Zhenguo Hong, 2016. "Factors Influencing the Spatial Difference in Household Energy Consumption in China," Sustainability, MDPI, vol. 8(12), pages 1-20, December.
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    6. Huebner, Gesche M. & Hamilton, Ian & Chalabi, Zaid & Shipworth, David & Oreszczyn, Tadj, 2015. "Explaining domestic energy consumption – The comparative contribution of building factors, socio-demographics, behaviours and attitudes," Applied Energy, Elsevier, vol. 159(C), pages 589-600.
    7. Eggimann, Sven & Usher, Will & Eyre, Nick & Hall, Jim W., 2020. "How weather affects energy demand variability in the transition towards sustainable heating," Energy, Elsevier, vol. 195(C).
    8. Yuanzheng Li & Wenjing Wang & Yating Wang & Yashu Xin & Tian He & Guosong Zhao, 2020. "A Review of Studies Involving the Effects of Climate Change on the Energy Consumption for Building Heating and Cooling," IJERPH, MDPI, vol. 18(1), pages 1-18, December.
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    11. Kočí, Jan & Kočí, Václav & Maděra, Jiří & Černý, Robert, 2019. "Effect of applied weather data sets in simulation of building energy demands: Comparison of design years with recent weather data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 22-32.
    12. Ye, Zhongnan & Cheng, Kuangly & Hsu, Shu-Chien & Wei, Hsi-Hsien & Cheung, Clara Man, 2021. "Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach," Applied Energy, Elsevier, vol. 301(C).
    13. Li, Jianglong & Yang, Lisha & Long, Houyin, 2018. "Climatic impacts on energy consumption: Intensive and extensive margins," Energy Economics, Elsevier, vol. 71(C), pages 332-343.
    14. Khuram Pervez Amber & Muhammad Waqar Aslam & Faraz Ikram & Anila Kousar & Hafiz Muhammad Ali & Naveed Akram & Kamran Afzal & Haroon Mushtaq, 2018. "Heating and Cooling Degree-Days Maps of Pakistan," Energies, MDPI, vol. 11(1), pages 1-12, January.
    15. 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.
    16. Copiello, Sergio & Grillenzoni, Carlo, 2017. "Is the cold the only reason why we heat our homes? Empirical evidence from spatial series data," Applied Energy, Elsevier, vol. 193(C), pages 491-506.
    17. Yassaghi, Hamed & Gurian, Patrick L. & Hoque, Simi, 2020. "Propagating downscaled future weather file uncertainties into building energy use," Applied Energy, Elsevier, vol. 278(C).
    18. Nahid-Al-Masood, & Yan, Ruifeng & Saha, Tapan Kumar, 2015. "A new tool to estimate maximum wind power penetration level: In perspective of frequency response adequacy," Applied Energy, Elsevier, vol. 154(C), pages 209-220.
    19. Mahuya Basu & Tanupa Chakraborty, 2019. "Weather risk assessment of Indian power sector: A conditional value-at-risk approach," Energy & Environment, , vol. 30(4), pages 641-661, June.
    20. Lawal, Abiola S. & Servadio, Joseph L. & Davis, Tate & Ramaswami, Anu & Botchwey, Nisha & Russell, Armistead G., 2021. "Orthogonalization and machine learning methods for residential energy estimation with social and economic indicators," Applied Energy, Elsevier, vol. 283(C).
    21. Streltsov, Artem & Malof, Jordan M. & Huang, Bohao & Bradbury, Kyle, 2020. "Estimating residential building energy consumption using overhead imagery," Applied Energy, Elsevier, vol. 280(C).
    22. Safarzadeh, Soroush & Rasti-Barzoki, Morteza, 2019. "A game theoretic approach for assessing residential energy-efficiency program considering rebound, consumer behavior, and government policies," Applied Energy, Elsevier, vol. 233, pages 44-61.
    23. Piotr Michalak, 2021. "Selected Aspects of Indoor Climate in a Passive Office Building with a Thermally Activated Building System: A Case Study from Poland," Energies, MDPI, vol. 14(4), pages 1-22, February.

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