Decision-support method for profitable residential energy retrofitting based on energy-related occupant behaviour

JOURNAL OF CLEANER PRODUCTION

卷:222

Article

Serrano-Jimenez, Antonio¹  Lizana, Jesus¹  Molina-Huelva, Marta²  Barrios-Padura, Angela¹ 

[1] Univ Seville, Dept Construcc Arquitecton 1, Ave Reina Mercedes 2, E-41012 Seville, Spain

[2] Univ Seville, IUACC, Ave Reina Mercedes 2, E-41012 Seville, Spain

关键词: Energy renovation;    Low-carbon energy;    Occupant behaviour;    Consumption scenarios;    Decision-making;    Multi-family buildings;   

DOI  :  10.1016/j.jclepro.2019.03.089

来源: Elsevier

【 摘 要 】

Low-carbon energy retrofitting in buildings plays an important role because 75% of European buildings are considered inefficient and more than 40% are currently over 50 years old. The economic reinvestment of energy retrofit actions through reduced energy bills, as energy directives promote, greatly depends upon the energy consumption patterns. In support of the decision-making process towards a low-carbon energy transition in multi-family buildings, this paper introduces a novel assessment method that evaluates the profitability of energy efficiency measures, according to standard operating conditions derived from energy performance certificate procedures and real occupant energy consumption scenarios, through a parametric analysis. The aim is to assess the real energy and economic savings of retrofitting actions, depending on different energy-related occupant scenarios, and to enable comparisons with other buildings, providing a valuable model to identify the most feasible and low-carbon energy strategies in residential energy retrofitting. A Spanish multi-family building from 1942 is taken as the reference case study. The results show that energy savings for dwellings vary up to 80%, and the net present value per dwelling differs by up to 20,000 between different energy consumption patterns. The most appropriate energy efficiency measures according to low, medium or high consumption scenarios are highlighted. (C) 2019 Elsevier Ltd. All rights reserved.

【 授权许可】

Free