Investment decision-making optimization of energy efficiency retrofit measures in multiple buildings under financing budgetary restraint

被引:64
作者
He, Yong [1 ]
Liao, Nuo [1 ]
Bi, Jiajing [2 ]
Guo, Liwei [3 ]
机构
[1] Guangdong Univ Technol, Sch Management, Guangzhou 510520, Guangdong, Peoples R China
[2] Univ Delaware, Ctr Energy & Environm Policy, Newark, DE 19716 USA
[3] Hangzhou Polytech, Sch Business, Hangzhou 311402, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
Buildings energy efficiency; Energy efficiency retrofit; Investment decision-making; Multi-objective optimization; Intelligent algorithm; MULTIOBJECTIVE OPTIMIZATION; RESIDENTIAL BUILDINGS; EXISTING BUILDINGS; PUBLIC BUILDINGS; COST; MODEL; CRITERIA; DESIGN; SUSTAINABILITY; REFURBISHMENT;
D O I
10.1016/j.jclepro.2019.01.119
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Regarding to energy efficiency retrofit investment to numerous buildings, which buildings should be invested and which of the retrofit measures should be implemented for investable buildings are challenging tasks. The current literature have studied on choosing energy efficiency retrofit measures in single building, relatively little attention has been paid to the retrofit investment decision-making in multiple buildings. In addition, the existing studies almost put the retrofit cost as an objective that need to be minimized, and the retrofit capital budget is not taken into consideration. This paper proposes a decision-making optimization framework for energy efficiency retrofit investment in numerous buildings under financing budgetary restraint. A multi-objective optimization model with the economic goals being the net present value and time of return, and the environmental goals being the energy saving and emission reduction is presented, and then the intelligent optimization method combing particle swarm optimization and genetic algorithm is designed to search the retrofit investment strategy. The obtained investment strategy could determine which of the buildings should be invested to retrofit, and the combination of retrofitting measures for every investable building. An empirical study is conducted on 27 buildings of non-governmental organization in the state of Delaware in the United States, and the results indicate that the validity of the proposed framework. The findings indicate that the framework is an effective approach to assist the sustainability goal at regional level. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1078 / 1094
页数:17
相关论文
共 44 条
[1]  
AGDI 2013. Australian Government Department of Industry, 2013, REV OP EFF COMM BUIL
[2]   Cost effective energy and carbon emissions optimization in building renovation (Annex 56) [J].
Almeida, Manuela ;
Ferreira, Marco .
ENERGY AND BUILDINGS, 2017, 152 :718-738
[3]  
[Anonymous], 2009, Buildings and Climate Change: Summary for Decision Makers
[4]   Energy and environmental benefits in public buildings as a result of retrofit actions [J].
Ardente, Fulvio ;
Beccali, Marco ;
Cellura, Maurizio ;
Mistretta, Marina .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2011, 15 (01) :460-470
[5]   Energy retrofit of educational buildings: Transient energy simulations, model calibration and multi-objective optimization towards nearly zero-energy performance [J].
Ascione, Fabrizio ;
Bianco, Nicola ;
De Masi, Rosa Francesca ;
Mauro, Gerardo Maria ;
Vanoli, Giuseppe Peter .
ENERGY AND BUILDINGS, 2017, 144 :303-319
[6]   A new methodology for cost-optimal analysis by means of the multi-objective optimization of building energy performance [J].
Ascione, Fabrizio ;
Bianco, Nicola ;
De Stasio, Claudio ;
Mauro, Gerardo Maria ;
Vanoli, Giuseppe Peter .
ENERGY AND BUILDINGS, 2015, 88 :78-90
[7]   A Decentralized Electricity Market Scheme Enabling Demand Response Deployment [J].
Bahrami, Shahab ;
Amini, M. Hadi ;
Shafie-khah, Miadreza ;
Catalao, Joao P. S. .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2018, 33 (04) :4218-4227
[8]   Role of financial mechanisms for accelerating the rate of water and energy efficiency retrofits in Australian public buildings: Hybrid Bayesian Network and System Dynamics modelling approach [J].
Bertone, Edoardo ;
Sahin, Oz ;
Stewart, Rodney A. ;
Zou, Patrick X. W. ;
Alam, Morshed ;
Hampson, Keith ;
Blair, Evan .
APPLIED ENERGY, 2018, 210 :409-419
[9]   Architecture: Architects of a low-energy future [J].
Butler, Declan .
NATURE, 2008, 452 (7187) :520-523
[10]   Effectiveness of single and multiple energy retrofit measures on the energy consumption of office buildings [J].
Chidiac, S. E. ;
Catania, E. J. C. ;
Morofsky, E. ;
Foo, S. .
ENERGY, 2011, 36 (08) :5037-5052