Abstract
This chapter aims to develop a decision framework for finding the best energy retrofit strategy for buildings, which are responsible for a considerable amount of energy consumption around the world. Building energy retrofit can be financially beneficial to the stakeholders and reduce the footprint of human life on the environment. The proposed framework includes essential economic and environmental criteria such as project profitability, maintenance costs, and the energy-saving obtained by renovating the inefficient facilities with better alternatives. The uncertainty of input data is taken into account to reduce the risk of investment and increase the project’s resiliency against possible changes in critical parameters. The framework is also used for a real academic building as a case study, which caused the building to experience a remarkable reduction in the total energy consumption. The framework of this chapter is a generalized model, which helps to get familiar with the process of developing similar frameworks for building energy retrofit analysis. It can also be extended according to the needs of the decision-maker and the circumstances of each project.
Similar content being viewed by others
References
C.B. Aktas, M.M. Bilec, Impact of lifetime on US residential building LCA results. Int. J. Life Cycle Assess. 17(3), 337–349 (2012). https://doi.org/10.1007/s11367-011-0363-x
S. Bairamzadeh, M. Saidi-Mehrabad, M.S. Pishvaee, Modelling different types of uncertainty in biofuel supply network design and planning: A robust optimization approach. Renew. Energy 116, 500–517 (2018). https://doi.org/10.1016/j.renene.2017.09.020
M.R. Bussieck, A. Meeraus, General Algebraic Modeling System (GAMS) (2004). https://doi.org/10.1007/978-1-4613-0215-5_8
C.T. Chang, Multi-choice goal programming with utility functions. Eur. J. Oper. Res. 215(2), 439–445 (2011). https://doi.org/10.1016/j.ejor.2011.06.041
V. Diaby, K. Campbell, R. Goeree, Multi-criteria decision analysis (MCDA) in health care: A bibliometric analysis. Oper. Res Health Care 2, 20–24 (2013). https://doi.org/10.1016/j.orhc.2013.03.001
Y. Fan, X. Xia, Building retrofit optimization models using notch test data considering energy performance certificate compliance. Appl. Energy 228, 2140–2152 (2018). https://doi.org/10.1016/j.apenergy.2018.07.043
R. Gregory, L. Failing, M. Harstone, G. Long, T. McDaniels, D. Ohlson, Structured decision making: A practical guide to environmental management choices, in Structured Decision Making: A Practical Guide to Environmental Management Choices, (2012). https://doi.org/10.1002/9781444398557
B. Güçyeter, H.M. Günaydin, Optimization of an envelope retrofit strategy for an existing office building. Energ. Buildings 55, 647–659 (2012). https://doi.org/10.1016/j.enbuild.2012.09.031
Y. He, N. Liao, J. Bi, L. Guo, Investment decision-making optimization of energy efficiency retrofit measures in multiple buildings under financing budgetary restraint. J. Clean. Prod. 215, 1078–1094 (2019). https://doi.org/10.1016/j.jclepro.2019.01.119
Y. Huang, J.L. Niu, T.M. Chung, Study on performance of energy-efficient retrofitting measures on commercial building external walls in cooling-dominant cities. Appl. Energy 103, 97–108 (2013). https://doi.org/10.1016/j.apenergy.2012.09.003
P. Jie, F. Zhang, Z. Fang, H. Wang, Y. Zhao, Optimizing the insulation thickness of walls and roofs of existing buildings based on primary energy consumption, global cost and pollutant emissions. Energy 159, 1132–1147 (2018). https://doi.org/10.1016/j.energy.2018.06.179
L. Kang, Y. Liu, Multi-objective optimization on a heat exchanger network retrofit with a heat pump and analysis of CO2 emissions control. Appl. Energy 154, 696–708 (2015). https://doi.org/10.1016/j.apenergy.2015.05.050
M. Khoukhi, A.F. Darsaleh, S. Ali, Retrofitting an existing office building in the UAE towards achieving low-energy building. Sustainability (Switzerland) 12(6) (2020). https://doi.org/10.3390/su12062573
Z. Ma, P. Cooper, D. Daly, L. Ledo, Existing building retrofits: Methodology and state-of-the-art. Energ. Buildings 55, 889–902 (2012). https://doi.org/10.1016/j.enbuild.2012.08.018
M. Motalebi, M.M. Nasiri, G.H. Shakouri, H. Taghaddos, A simulation-optimization model for solar PV panel selection under solar irradiance and load uncertainty. Adv. Ind. Eng. 54(2), 141–164 (2020). https://doi.org/10.22059/jieng.2021.323127.1760
M. Pazouki, K. Rezaie, A. Bozorgi-Amiri, A fuzzy robust multi-objective optimization model for building energy retrofit considering utility function: a university building case study. Energ. Buildings 241, 110933 (2021). https://doi.org/10.1016/j.enbuild.2021.110933
S. Roberts, Altering existing buildings in the UK. Energy Policy 36(12), 4482–4486 (2008). https://doi.org/10.1016/j.enpol.2008.09.023
J. Si, L. Marjanovic-Halburd, F. Nasiri, S. Bell, Assessment of building-integrated green technologies: A review and case study on applications of Multi-Criteria Decision Making (MCDM) method. Sustain. Cities Soc. 27, 106–115 (2016). https://doi.org/10.1016/j.scs.2016.06.013
M. Talaei, B. Farhang Moghaddam, M.S. Pishvaee, A. Bozorgi-Amiri, S. Gholamnejad, A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: A numerical illustration in electronics industry. J. Clean. Prod. 113, 662–673 (2016). https://doi.org/10.1016/j.jclepro.2015.10.074
A. Tindale, Designbuilder Software 3.4.0.041 (Design-Builder Software Ltd, 2014)
T. Walter, M.D. Sohn, A regression-based approach to estimating retrofit savings using the building performance database. Appl. Energy 179, 996–1005 (2016). https://doi.org/10.1016/j.apenergy.2016.07.087
J. Zhai, N. Leclaire, M. Bendewald, Deep energy retrofit of commercial buildings: A key pathway toward low-carbon cities. Carbon Manage. 2(4), 425–430 (2011). https://doi.org/10.4155/cmt.11.35
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this entry
Cite this entry
Pazouki, M., Bozorgi-Amiri, A. (2021). Mathematical Modeling and Simulation Validation in Optimizing Multi-objective Energy Systems Performance. In: Fathi, M., Zio, E., Pardalos, P.M. (eds) Handbook of Smart Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-72322-4_110-1
Download citation
DOI: https://doi.org/10.1007/978-3-030-72322-4_110-1
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72322-4
Online ISBN: 978-3-030-72322-4
eBook Packages: Springer Reference Economics and FinanceReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences