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Is a self-sufficient building energy efficient? Lesson learned from a case study in Mediterranean climate

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  • de Rubeis, Tullio
  • Nardi, Iole
  • Ambrosini, Dario
  • Paoletti, Domenica

Abstract

In this paper, the energy performance of an energy self-sufficient residential building, located in the outskirt of L’Aquila city, in central Italy, is presented. The aim of this study is to demonstrate that even a self- sufficient building, that represents a still uncommon construction in the Mediterranean area, can have optimization margins. The analysis is performed via calibrated model carried out with EnergyPlus™ engine coupled with DesignBuilder. The results of this study show a substantial discrepancy between the high efficiency of the envelope, and the poor performance of the heating system. Therefore, five possible scenarios, that differ for the technological plant, were hypothesized. Based on the total thermal energy consumption of the heating system as it is, equal to 29.9 kWh/m2 y, the proposed multi-scenario analysis allows to identify a solution that ensures energy savings up to 67.1%. In this study, these results indicate that a proper a priori study of building’s thermal load by considering different plant typologies could help to choose the better solutions for realizing efficient buildings.

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  • de Rubeis, Tullio & Nardi, Iole & Ambrosini, Dario & Paoletti, Domenica, 2018. "Is a self-sufficient building energy efficient? Lesson learned from a case study in Mediterranean climate," Applied Energy, Elsevier, vol. 218(C), pages 131-145.
    • Handle: RePEc:eee:appene:v:218:y:2018:i:c:p:131-145
    DOI: 10.1016/j.apenergy.2018.02.166
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    5. 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.
    6. Wu, Xianguo & Feng, Zongbao & Chen, Hongyu & Qin, Yawei & Zheng, Shiyi & Wang, Lei & Liu, Yang & Skibniewski, Miroslaw J., 2022. "Intelligent optimization framework of near zero energy consumption building performance based on a hybrid machine learning algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).

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