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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

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  • Beccali, Marco
  • Ciulla, Giuseppina
  • Lo Brano, Valerio
  • Galatioto, Alessandra
  • Bonomolo, Marina

Abstract

The public buildings sector represents one of the most intensive items of EU energy consumption; the application of retrofit solutions in existing buildings is a crucial way to reduce its impact. To facilitate the knowledge of the energy performance of existing non-residential buildings and the choice of the more adequate actions, Public Administrations (PA) should have the availability of proper tools. Within the Italian project “POI 2007-13”, a database and a decision support tool, for easy use, even to a non-technical user, have been developed. A large set of data, obtained from the energy audits of 151 existing public buildings located in four regions of South Italy have been analysed, elaborated, and organised in a database. This was used to identify the best architectures of two ANNs and to train them. The first ANN provides the actual energy performance of any building; the second ANN assesses key economic indicators. A decision support tool, based on the use of these ANNs is conceived for a fast prediction of the energy performance of buildings and for a first selection of energy retrofit actions that can be applied.

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  • 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.
    • Handle: RePEc:eee:energy:v:137:y:2017:i:c:p:1201-1218
    DOI: 10.1016/j.energy.2017.05.200
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    1. 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.
    2. Ochoa, Carlos E. & Capeluto, I. Guedi, 2015. "Decision methodology for the development of an expert system applied in an adaptable energy retrofit façade system for residential buildings," Renewable Energy, Elsevier, vol. 78(C), pages 498-508.
    3. Khan, N. & Abas, N., 2011. "Comparative study of energy saving light sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 296-309, January.
    4. Liu, Wenling & Zhang, Jinyun & Bluemling, Bettina & Mol, Arthur P.J. & Wang, Can, 2015. "Public participation in energy saving retrofitting of residential buildings in China," Applied Energy, Elsevier, vol. 147(C), pages 287-296.
    5. Santamouris, M. & Balaras, C.A. & Dascalaki, E. & Argiriou, A. & Gaglia, A., 1994. "Energy consumption and the potential for energy conservation in school buildings in Hellas," Energy, Elsevier, vol. 19(6), pages 653-660.
    6. Galatioto, A. & Beccali, M., 2016. "Aspects and issues of daylighting assessment: A review study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 852-860.
    7. Beccali, Marco & Bonomolo, Marina & Ciulla, Giuseppina & Galatioto, Alessandra & Lo Brano, Valerio, 2015. "Improvement of energy efficiency and quality of street lighting in South Italy as an action of Sustainable Energy Action Plans. The case study of Comiso (RG)," Energy, Elsevier, vol. 92(P3), pages 394-408.
    8. Higgins, Andrew & Syme, Mike & McGregor, James & Marquez, Leorey & Seo, Seongwon, 2014. "Forecasting uptake of retrofit packages in office building stock under government incentives," Energy Policy, Elsevier, vol. 65(C), pages 501-511.
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