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Energy flexibility for heating and cooling based on seasonal occupant thermal adaptation in mixed-mode residential buildings

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  • Du, Chenqiu
  • Li, Baizhan
  • Yu, Wei
  • Liu, Hong
  • Yao, Runming

Abstract

The energy flexibility for heating and cooling has not been fully explored though human thermal adaptation has been acknowledged in achieving energy savings in buildings. The aim of this study is to explore the capacity for heating/cooling flexibility in residential buildings in the hot summer and cold winter zone in China, by investigating the year-round dynamic changes in the thermal adaptation of occupants. A 13,005-set data set was extracted from a nation-wide field survey database. The results showed that the measured indoor temperatures were linearly related to the outdoor temperature in transient seasons but were discrete in the summer/winter seasons due to the mixed-mode operations of heating/cooling devices. The occupants’ neutral temperatures varied with outdoor temperatures in step with seasonal changes. Flexibility of temperature settings during the whole heating and cooling periods have been demonstrated, incorporating the dynamic thermal adaptation changes of occupants; such implementation has been estimated with great energy saving potential (e.g. 34.4% in Nanjing). This work contributes to the quantitative understanding of the role of human thermal adaptation in the smart control of residential energy management. It provides evidence for policy-making for flexible thermal design codes in building, to discourage excessive cooling/heating demands.

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  • Du, Chenqiu & Li, Baizhan & Yu, Wei & Liu, Hong & Yao, Runming, 2019. "Energy flexibility for heating and cooling based on seasonal occupant thermal adaptation in mixed-mode residential buildings," Energy, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219320341
    DOI: 10.1016/j.energy.2019.116339
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    References listed on IDEAS

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    Cited by:

    1. Xiaojun Liu & Xin Chen & Mehdi Shahrestani, 2020. "Optimization of Insulation Thickness of External Walls of Residential Buildings in Hot Summer and Cold Winter Zone of China," Sustainability, MDPI, vol. 12(4), pages 1-21, February.
    2. Coccia, Gianluca & Mugnini, Alice & Polonara, Fabio & Arteconi, Alessia, 2021. "Artificial-neural-network-based model predictive control to exploit energy flexibility in multi-energy systems comprising district cooling," Energy, Elsevier, vol. 222(C).
    3. O'Connell, Sarah & Reynders, Glenn & Keane, Marcus M., 2021. "Impact of source variability on flexibility for demand response," Energy, Elsevier, vol. 237(C).
    4. Yang, Zixu & Sun, Hongli & Wang, Baolong & Xiao, Hansong & Dong, Xian & Shi, Wenxing & Lin, Borong, 2022. "Experimental investigation on indoor environment and energy performance of convective terminals," Energy, Elsevier, vol. 251(C).
    5. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).
    6. Juan Sebastian Roncancio & José Vuelvas & Diego Patino & Carlos Adrián Correa-Flórez, 2022. "Flower Greenhouse Energy Management to Offer Local Flexibility Markets," Energies, MDPI, vol. 15(13), pages 1-20, June.

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