Developing a model for energy retrofit in large building portfolios: Energy assessment, optimization and uncertainty

被引:47
|
作者
Gabrielli, Laura [1 ]
Ruggeri, Aurora Greta [2 ]
机构
[1] Univ IUAV Venice, Dept Architecture & Arts, Dorsoduro 2206, I-30123 Venice, Italy
[2] Univ Padua, Dept Management & Engn, Stradella S Nicola 3, I-36100 Vicenza, Italy
关键词
Energy efficiency; Building portfolios; Linear regression; Multi-attribute optimization; Life cycle costing; Uncertainty; PROJECT EVALUATION TECHNIQUES; RESIDENTIAL SECTOR; REGRESSION-ANALYSIS; LIFE-CYCLE; DECISION-MAKING; PERFORMANCE GAP; RISK-ASSESSMENT; HOUSING STOCK; EFFICIENCY; CONSUMPTION;
D O I
10.1016/j.enbuild.2019.109356
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
In recent years, an increasing interest has been addressed to the energy efficiency of buildings. However, the focus has always been on individual properties, while both literature and practice still lack detailed solutions for entire stocks. Conversely, energy efficiency approaches should consider building portfolios as a whole, given that the deep decarbonisation of existing stocks would bring significant environmental and economic benefits. In this research paper, a decision support model has been developed for planning and managing energy retrofit operations in wide building portfolios. The core idea is to go beyond the single-building perspective and identify the level of energy retrofit that leads to the greatest possible benefit, comprising economic, environmental, human, and conservation targets. Energy improvement is here handled as an optimisation process, showing conflicting objectives and constraints. To this end, several techniques have been combined from various disciplines such as statistics, economics, energy modelling, operational research, and risk analysis, to develop a comprehensive and interdisciplinary methodology. Among the results of this research, the extreme flexibility in comparing countless design scenarios, the limited amount of buildings' characteristics required and the straightforwardness of the model developed are the most important contributions achieved. (C) 2019 Elsevier B.V. All rights reserved.
引用
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页数:23
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