Planning energy retrofit on historic building stocks: A score-driven decision support system

被引:29
|
作者
Ruggeri, Aurora Greta [1 ]
Calzolari, Marta [2 ]
Scarpa, Massimiliano [3 ]
Gabrielli, Laura [3 ]
Davoli, Pietromaria [4 ]
机构
[1] Univ Padua, Dept Management Engn, Stradella S Nicola 3, I-36100 Vicenza, Italy
[2] Univ Parma, Dept Engn & Architecture, Parco Area Sci 181A, I-43124 Parma, Italy
[3] Univ IUAV Venice, Dept Architecture & Arts, Dorsoduro 2206, I-30123 Venice, Italy
[4] Univ Ferrara, Dept Architecture, Via Ghiara 36, I-44121 Ferrara, Italy
关键词
Historic buildings; Decision-making; Energy retrofit; Analytic hierarchy process; Multi-criteria analysis; Life cycle costing; Building stocks; PROJECT EVALUATION TECHNIQUES; EFFICIENCY MEASURES; THERMAL COMFORT; TRADITIONAL BUILDINGS; PERFORMANCE; METHODOLOGY; CONSUMPTION; IMPROVEMENT; COMPENDIUM; IMPACT;
D O I
10.1016/j.enbuild.2020.110066
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Among the policies and regulations aimed at reducing the energy use in building stocks, the retrofit of historic buildings is one of the hardest challenges both for research and practice, because it is necessary to combine the traditional energy/economy targets with safeguard programs and conservation theories. In this paper, a decision support system is developed in order to plan and manage energy retrofit campaigns tailored for cultural heritage. Costs and energy uses are assessed, as well as the compatibility of interventions and their impact on indoor environmental quality. The energy use is seen under an economic, environmental, human and cultural perspective, providing a decision-making procedure that could be very useful for asset holders, public or private investors as well as for portfolio managers or agencies. The most important achievements in this work are the assessment of a so-called restoration score as a way to include conservation aspects in the selection procedure, and the integration of different appraisal techniques such as multi-attribute analysis, life cycle costing and analytic hierarchy process. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页数:19
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