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Modelling relationship among energy demand, climate and office building features: A cluster analysis at European level

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  • Ciulla, Giuseppina
  • Lo Brano, Valerio
  • D’Amico, Antonino

Abstract

More than one-third of the energy demand of industrialised countries is due to achieving acceptable conditions of thermal comfort and lighting in buildings. Energy demand in buildings depends on a combination of several parameters, such as climate, envelope typologies, occupant behaviour, and intended use. Indeed, assessing a building’s energy performance requires substantial input data describing constructions, environmental conditions, envelope thermo-physical properties, geometry, control strategies, and several other parameters. This has been a very active area of research in recent years, and several numerical approaches have been developed for building simulation; furthermore, most of these approaches have been tested and implemented in specialised software tools. However, the use of these tools poses many challenges in regards to the retrieval of reliable and detailed information, setting a steep learning curve for engineers and energy managers. It is often more convenient to have a simplified model that allows the evaluation of energy demand with a good level of accuracy and without excessive computational costs or user expertise. In this work, the authors extrapolate a set of simple correlations to permit a fast preliminary assessment of heating energy demand for office buildings. Data employed to build the correlations come from detailed dynamic simulations performed in TRNSYS environment. The models were built according to the standards and laws of building energy requirements in seven different European countries. For a more general assessment, the authors identified three cities for each country; for each location, three models with different shape factors were considered (S/V=0.24, 0.5 and 0.9). The results obtained from the simulations allowed for the determination of direct correlations among the thermal energy demand for space heating HDD and S/V values. In this way, the authors provided simple equations for a reliable and easy-to-use preliminary assessment of the energy demand of non-residential buildings to planners and designers, taking into account regulation dictated by law in each considered country.

Suggested Citation

  • Ciulla, Giuseppina & Lo Brano, Valerio & D’Amico, Antonino, 2016. "Modelling relationship among energy demand, climate and office building features: A cluster analysis at European level," Applied Energy, Elsevier, vol. 183(C), pages 1021-1034.
    • Handle: RePEc:eee:appene:v:183:y:2016:i:c:p:1021-1034
    DOI: 10.1016/j.apenergy.2016.09.046
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    Cited by:

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    5. Meizhen Zhang & Tao Lv & Xu Deng & Yuanxu Dai & Muhammad Sajid, 2019. "Diffusion of China’s coal-fired power generation technologies: historical evolution and development trends," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(1), pages 7-23, January.
    6. Antonino D’Amico & Domenico Panno & Giuseppina Ciulla & Antonio Messineo, 2020. "Multi-Energy School System for Seasonal Use in the Mediterranean Area," Sustainability, MDPI, vol. 12(20), pages 1-27, October.
    7. Beccali, Marco & Ciulla, Giuseppina & Lo Brano, Valerio & Galatioto, Alessandra & Bonomolo, Marina, 2017. "Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy," Energy, Elsevier, vol. 137(C), pages 1201-1218.
    8. Rafael Sánchez-Durán & Joaquín Luque & Julio Barbancho, 2019. "Long-Term Demand Forecasting in a Scenario of Energy Transition," Energies, MDPI, vol. 12(16), pages 1-23, August.
    9. 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.
    10. Ciulla, G. & D'Amico, A. & Lo Brano, V. & Traverso, M., 2019. "Application of optimized artificial intelligence algorithm to evaluate the heating energy demand of non-residential buildings at European level," Energy, Elsevier, vol. 176(C), pages 380-391.
    11. Matteo Rivoire & Alessandro Casasso & Bruno Piga & Rajandrea Sethi, 2018. "Assessment of Energetic, Economic and Environmental Performance of Ground-Coupled Heat Pumps," Energies, MDPI, vol. 11(8), pages 1-23, July.
    12. Galatioto, A. & Ciulla, G. & Ricciu, R., 2017. "An overview of energy retrofit actions feasibility on Italian historical buildings," Energy, Elsevier, vol. 137(C), pages 991-1000.
    13. Yamaguchi, Yohei & Kim, Bumjoon & Kitamura, Takuya & Akizawa, Kotone & Chen, Hemiao & Shimoda, Yoshiyuki, 2022. "Building stock energy modeling considering building system composition and long-term change for climate change mitigation of commercial building stocks," Applied Energy, Elsevier, vol. 306(PA).
    14. Roberto Zanetti Freire & Gerson Henrique dos Santos & Leandro dos Santos Coelho, 2017. "Hygrothermal Dynamic and Mould Growth Risk Predictions for Concrete Tiles by Using Least Squares Support Vector Machines," Energies, MDPI, vol. 10(8), pages 1-16, July.
    15. Ana Ogando & Natalia Cid & Marta Fernández, 2017. "Energy Modelling and Automated Calibrations of Ancient Building Simulations: A Case Study of a School in the Northwest of Spain," Energies, MDPI, vol. 10(6), pages 1-17, June.
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