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Review

Decision-Making Approach to Urban Energy Retrofit—A Comprehensive Review

by
Lei Shu
* and
Dong Zhao
School of Planning, Design, and Construction, Michigan State University, 552 W Circle Dr, East Lansing 48824, MI, USA
*
Author to whom correspondence should be addressed.
Buildings 2023, 13(6), 1425; https://doi.org/10.3390/buildings13061425
Submission received: 9 May 2023 / Revised: 29 May 2023 / Accepted: 30 May 2023 / Published: 31 May 2023
(This article belongs to the Special Issue Building Energy Retrofit for Sustainable Urban Renewal)
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Abstract

:
This research presents a comprehensive review of the research on smart urban energy retrofit decision-making. Based on the analysis of 91 journal articles over the past decade, the study identifies and discusses five key categories of approaches to retrofit decision-making, including simulation, optimization, assessment, system integration, and empirical study. While substantial advancements have been made in this field, opportunities for further growth remain. Findings suggest directions for future research and underscore the importance of interdisciplinary collaboration, data-driven evaluation methodologies, stakeholder engagement, system integration, and robust and adaptable retrofit solutions in the field of urban energy retrofitting. This review provides valuable insights for researchers, policymakers, and practitioners interested in advancing the state of the art in this critical area of research to facilitate more effective, sustainable, and efficient solutions for urban energy retrofits.

1. Introduction

The demand for energy increases when cities expand, and the increasing energy consumption in buildings and cities increases greenhouse gas (GHG) emissions and contributes to climate change. Growing concerns have been attached to reducing GHG emissions over the last few decades to avoid climate deterioration. Cities are the largest energy consumers, accounting for 78% of energy consumption and generating 60% of GHG emissions [1]. The transition for cities towards a high-energy-efficiency and low-carbon scenario, such as the green building movement [2], can significantly promote district and national sustainable development. Urban energy retrofit is an important step toward a sustainable transition. Over the last few years, urban energy retrofits have been researched on individual buildings or at a district scale. Interaction effects of building technologies and occupant behaviors on building energy consumption pose challenges for conducting building energy retrofits [3,4]. Appropriate retrofit solutions for large-scale buildings must also consider energy network allocation [5] and building interactions [6]. Policymakers face challenges when deciding how to proceed with urban energy retrofitting. Therefore, this research aims to systematically review the literature on urban energy retrofit decision-making in the last decade. The review categorizes and discusses the following five decision approaches: 1. evaluating different energy retrofit scenarios based on energy simulation; 2. determining optimal retrofit solutions through optimization models; 3. assessing urban building energy performance through data analytics; 4. supporting retrofit decision-making through integrated systems; 5. obtaining retrofit experience from empirical projects.

2. Methods

To comprehensively review the literature of urban energy retrofit decision-making research, clusters of synonyms for keywords were identified and used for screening satisfied research. “Renovate” and “refurbish” were used as alternatives to “retrofit”. “Community”, “neighborhood”, “district”, “urban”, “regional”, and “city” were to constrain the retrofit to a large district scale. “Smart”, “AI”, “intelligent”, and “artificial intelligence” were used as one keyword cluster to target the smart solutions. Peer-reviewed journals ranging from 2012 to 2022 were searched by keywords, titles, and abstracts on the Web of Science and Scopus databases. Then, articles that focus on smart decision-making from an energy policymakers’ perspective were selected. In total, 91 peer-reviewed papers about smart urban energy retrofit decision-making solutions were identified to be thoroughly reviewed. These papers were published in 43 journals, and the journals that covered three or more reviewed articles are listed in Table 1. Table 1 shows that Energy and Buildings contains the most research papers, with a total of 15.

3. Data

Figure 1 shows the number of publications each year. The number of papers published each year gradually increased with slight fluctuations. The surge in the number of papers published in 2017 could be attributed to the adoption of the Paris Climate Agreement in 2015 and the United Nations’ Sustainable Development Goals (SDGs) in 2016. In addition, a few research papers—published in late 2022 and available in 2023—were not included in this research, which resulted in a decrease in the number of journal articles observed in 2022.
Figure 2 displays the countries of affiliation for the reviewed papers. These studies were conducted in 29 different countries or regions. Italy held the largest portion, accounting for 26% of urban energy retrofit decision-making research. This is not surprising given Italy’s rich cultural heritage, where preserving historic properties and making them more energy efficient are important. China followed with 9% of the research, while the UK and Switzerland equally held 7%. The remaining 6% of the research was conducted separately in the US and Canada. These six countries conducted more than half of the research about smart urban energy retrofit decision-making.
Figure 3 shows the eight major expertise fields of the authors of the reviewed publications, indicating that research on urban building energy retrofit is highly interdisciplinary. The expertise fields in which more than one investigator participated were only counted once in each research paper. Researchers from architecture, construction, and civil engineering accounted for 31% of the total. When combining the researchers from energy and environmental engineering (19%), they summed up to 50% together and indicated a strong role of these disciplines in advancing urban energy retrofit research. The remaining fields and their share of the research in descending order were industry and industrial engineering (14%), urban planning (11%), electrical engineering and computer science (11%), mechanical engineering (8%), business (5%), and sociology (1%). The relatively low share percentages suggested greater opportunities for potential interdisciplinary collaborations in urban energy retrofit research by involving researchers from these areas.

4. Results

Table 2 shows the summary of all research approaches in the reviewed papers. The table includes techniques used in each approach, remarks for each technique, and examples with references.

4.1. Energy Simulator-Based Approach

Energy simulators are used to evaluate building energy retrofit measures and help policymakers select reasonable retrofit solutions regarding energy savings, CO2 emissions, and the cost of retrofit solutions. The baseline building energy performance can be obtained by accessing the urban energy database or building the baseline urban building model. Then, tested retrofit measures, such as improving building envelope insulation, replacing windows, changing heating, ventilation, and air conditioning (HVAC) systems, and integrating renewable energy systems, are implemented on the baseline model. By simulating the energy consumption of the urban building model with various retrofit solutions, the effectiveness of different retrofits and their combinations can be assessed. The following various software tools can be used to perform these simulations.
EnergyPlus is a widely used energy simulation engine that has supported various urban energy retrofit studies [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]. DesignBuilder is a user-friendly graphical interface for EnergyPlus that simplifies the modeling process. Numerous researchers utilized DesignBuilder to create visual building models and input data for building components and systems, and then executed energy simulations using the EnergyPlus engine [10,11,12,13,14,15,16,17,18,19,20,21]. Some researchers modeled buildings in Rhino and Grasshopper and then ran energy simulations in EnergyPlus [22,23]. Rhino and Grasshopper together form a powerful combination of 3D modeling and parametric design tools. They can help create and optimize urban building design solutions. Some researchers integrated MATLAB with EnergyPlus, which can help streamline the simulation process, postprocess results, and optimize energy retrofit performance [10,11,17,18,19,24,25,26]. EnergyPlus package also provides a Python API that allows researchers to set up optimization algorithms to explore various combinations of retrofit solutions [26,27,28,29].
In addition to EnergyPlus, which is widely used, there are alternative energy simulation tools that specialize in various aspects. Transient System Simulation Tool (TRNSYS) is widely used for simulating the behavior of energy systems, such as HVAC systems [30], solar thermal or photovoltaic (PV) systems [31,32], and other renewable energy technologies [33]. EnergyPLAN is an advanced energy system analysis software that can model grid flexibility and perform economic analysis. It was utilized for achieving large-scale energy system retrofits within a viable investment [34,35]. Integrated Environmental Solutions Virtual Environment (IES-VE) is a comprehensive building performance analysis software suite that incorporates a range of simulation and analysis tools. It was employed to investigate optimal decision-making for building energy retrofits [36,37]. IDA Indoor Climate and Energy (IDA ICE) is a building simulation software that was utilized to evaluate the potential of four large-scale building energy retrofit scenarios on the Finnish building stock [38]. ESP-r is an open-source building energy simulation software that was employed to develop strategies approaching large-scale nearly-zero energy targets [39]. Passive House Planning Package (PHPP) is a comprehensive energy modeling tool specifically for the design and certification of Passive House buildings. It was used to estimate the energy savings of measures [40]. Hybrid Optimization of Multiple Energy Resources (HOMER) allows for designing and analyzing hybrid power systems, and it was employed to explore the strategy for a net-zero energy building transition [41]. Moreover, the following self-developed energy simulation tools were applied in urban energy retrofit decision-making research: thermal balance-based building energy simulators were used to simulate urban building energy consumption [42,43,44]; geographic information system (GIS)-based solar energy calculation algorithms were developed to simulate the energy production of PV panels [45,46,47].
Overall, decision-making for urban retrofit solutions using energy simulators has a few benefits. This method is trustworthy because it is a physics-based simulation grounded in scientific principles and considers various factors, such as weather, human behavior, building materials, and energy system efficiency. In addition, weather data and occupant behavior parameters can be changed in the energy model to analyze their impacts on the same energy retrofit solutions. Considering the impact of weather on retrofit solutions helps make informed decisions for larger-scale energy retrofit projects that occupy multiple climate zones. Occupant behavior parameters can be easily changed to study human-relative retrofit solutions. For example, the window opening schedule can be changed to study its impact on the energy consumption of ventilation, heating, and cooling system. Then the window opening schedule corresponding to the minimum energy consumption can be suggested as a human-relative retrofit measure.
Despite its benefits, urban building energy modeling has limitations. It requires detailed input data that may not easily be available, such as the thickness and thermal resistance of different layers of the envelope insulation. Furthermore, inputting such details is time-consuming and requires expertise. More details lead to higher accuracy in simulation but can be computationally intensive, especially when dealing with large-scale urban environments. Most researchers only simulated representative individual buildings’ energy performance and projected the simulation results to an urban scale.

4.2. Optimization Modeling-Based Approach

Recent studies have explored various urban energy retrofit optimization models in different fields, including industrial and systems engineering (ISE), mechanical engineering (ME), and social, behavioral, and economic sciences (SBE).
In the ISE field, typical decision-making methods include the multi-criteria decision-making (MCDM) and multi-objective optimization (MOO) models, which were employed to address challenges associated with urban energy retrofit strategies. The mixed-integer linear programming (MILP) model was used to maximize the energy efficiency of district energy systems [48,49]. The analytical hierarchy process (AHP) model was widely applied to urban energy retrofits to tackle multiple conflicting criteria and decision alternatives [50,51,52]. The analytical network process (ANP) model, an extension of AHP to accommodate complex interdependencies among criteria and alternatives, was employed to prioritize urban retrofit solutions [53]. The data envelopment analysis (DEA) model—commonly used to empirically measure the productive efficiency of decision-making units—was utilized to evaluate the efficiency of building retrofit projects [54]. The measuring attractiveness by a categorical based evaluation technique (MACBETH) and the “Playing Cards” methods were used to define and analyze different urban scenarios [55]. The weighted sum model (WSM), a simple and widely used MCDM method, was used to decide the optimal energy retrofit plan for a whole stock of buildings [56]. The recurrent neural network (RNN) model has been trained to derive the cost-optimal retrofit solution [57]. The quality function deployment (QFD) framework was used to determine the best retrofit technologies with regard to stakeholders’ opinions [58]. Furthermore, a few optimization algorithms, such as the enhanced water strider optimization (EWSO) algorithm and the ant colony optimization (ACO) algorithm, were used on urban building energy operation and lifecycle optimization [59,60].
Some new decision-making models have been developed to target urban energy retrofit and planning. Keirstead and Calderon presented a technologies and urban resource networks (TURN) model to create an urban strategic energy plan with regard to spatial and temporal variations in energy demand [61]. Rinaldi et al. proposed an open-source sector coupling model (GRIMSEL-FLEX) to minimize the total urban energy cost for electricity and heating supply [62]. Luo and Oyedele proposed a novel data-driven life-cycle optimization model for urban building retrofitting [63]. Zheng et al. proposed a techno-economic-risk decision-making methodology (TERDMM) model that integrated life cycle cost analysis and Monte Carlo (MC) simulation [64]. Syal et al. developed an information model that can serve as the basis for an intelligent decision support system [65]. González et al. presented a maturity matrix assessment model of energy efficiency measures to determine future appropriate implementation [66]. Wang et al. developed a novel hybrid modeling approach to quantify the sustainability of retrofit solutions considering embodied energy and GHG emissions [67]. Stanica et al. proposed an integrative method to evaluate a large variety of energy conservation and renewable energy generation measures at different scales [68].
In the ME field, decision-making control models are essential for optimizing system performance. Various studies were performed to explore the energy-saving potential of intelligent control for a wide range of energy systems. For urban scale retrofit, a hierarchical decision-making strategy model was designed to manage the urban energy system that can help deal with the energy retrofit of urban subsystems as a whole in an integrated way [69]. A genetic algorithm (GA) was used to optimize the operation of pumping stations to achieve the minimum energy cost for water supply [70]. Several research explored the energy-saving feasibility of street lighting retrofits within the available budget and proposed methods aiming at maximizing energy reduction while achieving optimal allocation and light quality [71,72,73]. A decision-making control system was studied to realize the energy-saving goal of indoor lighting systems [74]. For building retrofits, an intelligent supervisory predictive control model of HVAC systems was proposed to minimize energy consumption without compromising occupants’ thermal comfort [75]. An expert system applied in an adaptable energy retrofit façade system of residential buildings was proposed to suggest suitable retrofit alternatives [76].
There is not much research about urban energy retrofits in the SBE field. Some models for assessing alternative retrofit solutions from socio-economic aspects were developed [77,78,79]. Some models for urban building energy retrofit plans were proposed considering workmanship capacity and population dynamics [80,81].
Overall, optimization modeling-based decision-making for urban energy retrofits were most widely used in the ISE field. ISE models focus on the performance of different urban energy systems and address techno-economic issues (sociotechnical balance). They aim to find the best solutions for reducing carbon emissions while considering the economic feasibility. ME Models aim to optimize the energy performance of a specific energy system. The focus of these models is on technical aspects (technological advancement). SBE models consider various social factors (social benefits), such as population growth, demographic changes, and labor constraints. These models aim to understand how social factors impact energy consumption patterns. They can help policymakers develop targeted strategies for energy reduction in a specific socio-demographic context.
Each of these fields has its own focus and approach to urban energy retrofit decision-making and optimization. It is essential to consider an integrated approach that combines the strengths of all three domains to develop comprehensive municipal energy retrofit strategies. This approach would address technical, economic, social, and environmental aspects of urban energy systems, leading to more holistic and sustainable solutions.

4.3. Assessment-Based Approach

Various approaches based on data analytics evaluate possible retrofit measure alternatives or predict potential energy savings. An integrated method for predicting the possibility of reducing urban energy consumption by using phase change material (PCM) in municipal heating networks was proposed [82]. A bottom-up evaluation approach was applied to study the techno-economic feasibility of the air-to-water heat pump retrofit in the housing stock [83]. A study was performed to estimate the best-case scenario for the benefits achievable depending on the urban green roof proportion [84]. Some models that could quantify the contribution of building characteristics and systems to energy consumption were investigated to infer the excepted energy savings [85,86,87]. Some researchers proposed comprehensive life cycle assessment approaches with the consideration of data uncertainties [88,89].
Overall, assessment-based decision-making relies on actual data from existing buildings, which can provide more realistic insights into building performance and energy consumption patterns. It can be applied to various scales, from individual buildings to entire urban areas, if the data for different scales are available. However, the performance of assessments based on data analytics can be constrained by data quality. Availability and expertise in data science may not be readily available for all researchers.

4.4. System Integration-Based Approach

Some systems were developed to support the urban energy retrofit decision-making process. Moghadam and Lombardi developed a multi-criteria spatial decision support systems (MC-SDSS) tool to identify and evaluate alternative urban energy scenarios from a long-term perspective [90]. Buffat et al. proposed a web-based decision support system (DSS) using a GIS-based building stock model [91]. Leandro et al. showed a platform that can help building owners and planners make informed decisions to improve building energy [92].
Overall, the introduced systems provide user-friendly interfaces for decision-making that enable householders and policymakers to easily access the different building retrofit solutions. However, few systems are available for users. More investment in urban energy system integration is necessary.

4.5. Empirical Study-Based Approach

Lessons from ongoing retrofit projects or completed projects can support policymakers’ decision-making. The decision-making process of private homeowners was investigated to improve the impact of policies leading to higher adoption of energy retrofit measures by homeowners [93]. A few frameworks assessing ongoing or completed urban energy renovation were proposed, through which lessons from projects could be concluded [94,95,96]. Jankovic introduced an innovative way to evaluate completed building retrofits through the simulation of dynamic heating tests with a calibrated model [97].
Overall, empirical studies can benefit the decision-making process with the consideration of practical problems. It can reveal unexpected challenges that may not be apparent in simulations and offer insights into the engagement and collaboration of stakeholders. However, lessons from urban retrofit projects may be limited due to the small number of completed projects, and the lessons may not be generalized to other situations.

5. Discussion and Future Directions

In this study, we present five distinct approaches, each with its own advantages, limitations, and optimal scenarios for implementation. The optimal choice among these approaches depends heavily on the specific conditions of each urban energy retrofit project. The nature of the project, the available data, and the socio-economic context can significantly influence which approach might be most suitable. Furthermore, in many cases, the best outcomes may be achieved through a combination of multiple approaches, leveraging the strengths of each.
We suggest the following six aspects that should be prioritized in the future research on smart urban energy retrofit decision-making.
  • Combine multidisciplinary expertise, such as in ISE, ME, and SBE domains, into the development of comprehensive, integrated decision-making models.
The siloed nature of research and limited interdisciplinary collaboration can hinder the development of comprehensive solutions. In the future, we should encourage interdisciplinary research, knowledge sharing, and collaboration between experts in different domains to create well-rounded decision-making models for urban energy retrofit projects.
2.
Enhance data-driven evaluation methodologies by improving data quality and accessibility.
It is suggested to establish data collection standards, promote data sharing, and develop advanced data analytics techniques to improve the accuracy of urban energy retrofit evaluations. This will allow for more exact analyses of urban energy demand and the potential efficient retrofit solutions.
3.
Carry out research on the generalization and applicability of the lessons learned from completed urban retrofit projects.
Generalization and applicability of the lessons from projects are challenging because of the limited number of completed retrofit projects and the lack of systematic approaches to knowledge transfer. In the future, we should prioritize the documentation of successful retrofit projects, develop frameworks for knowledge transfer, and invest in research that explores the applicability of best practices in different urban contexts.
4.
Investigate methods for collaborating and engaging stakeholders, including residents, building owners, intermediaries, and policymakers.
The understanding of stakeholders’ needs is still limited. In the future, we should develop stakeholder-centric frameworks, establish effective communication channels, and design incentive structures that encourage cooperation and active participation in urban energy retrofit projects. This will help ensure that urban energy retrofit projects achieve their desired outcomes.
5.
Consider how population migration and climate change may affect urban energy retrofitting strategies.
The lack of long-term strategic planning and understanding of the interplay between population dynamics and urban energy demand may have led to the gap in this research direction. In the future, we should invest in research that explores the complex relationships between climate change, population migration, and urban energy demand, and then develop robust and adaptable urban energy retrofit strategies that can withstand changing climatic and demographic conditions.
6.
Develop user-friendly urban energy retrofit systems that can empower householders and policymakers by providing them with easily accessible building retrofit solution plans.
There are only a few such systems available for use so far. This may be caused by the expensive investment in research and development. It is essential to invest more in urban energy system integration in the future, emphasizing the improvement of user-friendly interfaces for urban retrofit solutions. This will facilitate informed decision-making.

6. Limitations

Our review has two limitations. First, the keyword-based search strategy, which focused on “renovate”, “retrofit”, and “refurbish”, might exclude some relevant articles that did not utilize these specific terms. Second, our study concentrated on the decision-making process in urban energy retrofit rather than the specific retrofit solutions employed as a result of these decisions. The research regarding specific solutions for smart urban energy retrofits was outside the scope of this review.

7. Conclusions

This research presents a comprehensive review of the research on smart urban energy retrofit decision-making. A total of 91 journal papers over the last decade were reviewed. Results identified and discussed the following five categories of approaches to retrofit decision-making: simulation, optimization, assessment, system integration, and empirical study. The research on urban energy retrofit decision-making has made significant progress over the past ten years. However, there still exist opportunities for further development. Findings also inform a roadmap for future research to enable the development of more effective, sustainable, and efficient urban energy retrofit solutions, such as integrating decision-making methods, enhancing data availability, transferring knowledge from successful retrofit projects to other contexts, involving stakeholders in the decision-making process, studying the effects of population migration and climate change on urban energy retrofit strategies, and developing user-friendly decision-making systems.

Author Contributions

All authors have contributed with the same weight and effort. In detail, L.S. contributed the literature search and drafted the original manuscript. D.Z. provided the guidance for the literature search, suggested the article structure, reviewed the manuscript, and made revisions. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Science Foundation (NSF) through Grant #2046374. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the researchers and do not necessarily reflect the views of NSF.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Habitat, U. Cities and Pollution. Available online: https://www.un.org/en/climatechange/climate-solutions/cities-pollution (accessed on 15 March 2023).
  2. Zhao, D.; Miotto, A.B.; Syal, M.; Chen, J. Framework for Benchmarking green building movement: A case of Brazil. Sustain. Cities Soc. 2019, 48, 101545. [Google Scholar] [CrossRef]
  3. Zhao, D.; McCoy, A.P.; Du, J.; Agee, P.; Lu, Y. Interaction effects of building technology and resident behavior on energy consumption in residential buildings. Energy Build. 2017, 134, 223–233. [Google Scholar] [CrossRef]
  4. Mo, Y.; Zhao, D. Effective factors for residential building energy modeling using feature engineering. J. Build. Eng. 2021, 44, 102891. [Google Scholar] [CrossRef]
  5. Jing, R.; Wang, M.; Liang, H.; Wang, X.; Li, N.; Shah, N.; Zhao, Y. Multi-objective optimization of a neighborhood-level urban energy network: Considering Game-theory inspired multi-benefit allocation constraints. Appl. Energy 2018, 231, 534–548. [Google Scholar] [CrossRef]
  6. Luo, X.; Hong, T.; Tang, Y.-H. Modeling thermal interactions between buildings in an urban context. Energies 2020, 13, 2382. [Google Scholar] [CrossRef]
  7. Beccali, M.; Ciulla, G.; Di Pietra, B.; Galatioto, A.; Leone, G.; Piacentino, A. Assessing the feasibility of cogeneration retrofit and district heating/cooling networks in small Italian islands. Energy 2017, 141, 2572–2586. [Google Scholar] [CrossRef]
  8. Drouilles, J.; Aguacil, S.; Hoxha, E.; Jusselme, T.; Lufkin, S.; Rey, E. Environmental impact assessment of Swiss residential archetypes: A comparison of construction and mobility scenarios. Energy Effic. 2019, 12, 1661–1689. [Google Scholar] [CrossRef]
  9. Becchio, C.; Ferrando, D.G.; Fregonara, E.; Milani, N.; Quercia, C.; Serra, V. The cost-optimal methodology for the energy retrofit of an ex-industrial building located in Northern Italy. Energy Build. 2016, 127, 590–602. [Google Scholar] [CrossRef]
  10. Ascione, F.; Bianco, N.; Mauro, G.M.; Napolitano, D.F. Knowledge and energy retrofitting of neighborhoods and districts. A comprehensive approach coupling geographical information systems, building simulations and optimization engines. Energy Convers. Manag. 2021, 230, 113786. [Google Scholar] [CrossRef]
  11. Ascione, F.; Bianco, N.; Mauro, G.M.; Napolitano, D.F. Effects of global warming on energy retrofit planning of neighborhoods under stochastic human behavior. Energy Build. 2021, 250, 111306. [Google Scholar] [CrossRef]
  12. Monna, S.; Juaidi, A.; Abdallah, R.; Albatayneh, A.; Dutournie, P.; Jeguirim, M. Towards sustainable energy retrofitting, a simulation for potential energy use reduction in residential buildings in Palestine. Energies 2021, 14, 3876. [Google Scholar] [CrossRef]
  13. Yang, H.; Liu, L.; Li, X.; Liu, C.; Jones, P. Tailored domestic retrofit decision making towards integrated performance targets in Tianjin, China. Energy Build. 2017, 140, 480–500. [Google Scholar] [CrossRef]
  14. Chacón, L.; Chen Austin, M.; Castaño, C. A Multiobjective Optimization Approach for Retrofitting Decision-Making towards Achieving Net-Zero Energy Districts: A Numerical Case Study in a Tropical Climate. Smart Cities 2022, 5, 405–432. [Google Scholar] [CrossRef]
  15. Pasichnyi, O.; Levihn, F.; Shahrokni, H.; Wallin, J.; Kordas, O. Data-driven strategic planning of building energy retrofitting: The case of Stockholm. J. Clean. Prod. 2019, 233, 546–560. [Google Scholar] [CrossRef]
  16. Sharma, S.K.; Mohapatra, S.; Sharma, R.C.; Alturjman, S.; Altrjman, C.; Mostarda, L.; Stephan, T. Retrofitting Existing Buildings to Improve Energy Performance. Sustainability 2022, 14, 666. [Google Scholar] [CrossRef]
  17. Ascione, F.; Bianco, N.; Mauro, G.M.; Napolitano, D.F.; Vanoli, G.P. A multi-criteria approach to achieve constrained cost-optimal energy retrofits of buildings by mitigating climate change and urban overheating. Climate 2018, 6, 37. [Google Scholar] [CrossRef]
  18. Ascione, F.; Bianco, N.; De Masi, R.F.; Mauro, G.M.; Vanoli, G.P. Resilience of robust cost-optimal energy retrofit of buildings to global warming: A multi-stage, multi-objective approach. Energy Build. 2017, 153, 150–167. [Google Scholar] [CrossRef]
  19. Ascione, F.; Bianco, N.; De Stasio, C.; Mauro, G.M.; Vanoli, G.P. CASA, cost-optimal analysis by multi-objective optimisation and artificial neural networks: A new framework for the robust assessment of cost-optimal energy retrofit, feasible for any building. Energy Build. 2017, 146, 200–219. [Google Scholar] [CrossRef]
  20. Ashrafian, T.; Yilmaz, A.Z.; Corgnati, S.P.; Moazzen, N. Methodology to define cost-optimal level of architectural measures for energy efficient retrofits of existing detached residential buildings in Turkey. Energy Build. 2016, 120, 58–77. [Google Scholar] [CrossRef]
  21. Gabrielli, L.; Ruggeri, A.G. Developing a model for energy retrofit in large building portfolios: Energy assessment, optimization and uncertainty. Energy Build. 2019, 202, 109356. [Google Scholar] [CrossRef]
  22. Chang, S.; Yoshida, T.; Castro-Lacouture, D.; Yamagata, Y. Block-Level Building Transformation Strategies for Energy Efficiency, Thermal Comfort, and Visibility Using Bayesian Multilevel Modeling. J. Archit. Eng. 2021, 27, 05021008. [Google Scholar] [CrossRef]
  23. Camporeale, P.E.; Mercader-Moyano, P. A GIS-based methodology to increase energy flexibility in building cluster through deep renovation: A neighborhood in Seville. Energy Build. 2021, 231, 110573. [Google Scholar] [CrossRef]
  24. Thrampoulidis, E.; Mavromatidis, G.; Lucchi, A.; Orehounig, K. A machine learning-based surrogate model to approximate optimal building retrofit solutions. Appl. Energy 2021, 281, 116024. [Google Scholar] [CrossRef]
  25. Chang, S.; Castro-Lacouture, D.; Yamagata, Y. Decision support for retrofitting building envelopes using multi-objective optimization under uncertainties. J. Build. Eng. 2020, 32, 101413. [Google Scholar] [CrossRef]
  26. Zygmunt, M.; Gawin, D. Application of the Renewable Energy Sources at District Scale—A Case Study of the Suburban Area. Energies 2022, 15, 473. [Google Scholar] [CrossRef]
  27. Salata, F.; Ciancio, V.; Dell’Olmo, J.; Golasi, I.; Palusci, O.; Coppi, M. Effects of local conditions on the multi-variable and multi-objective energy optimization of residential buildings using genetic algorithms. Appl. Energy 2020, 260, 114289. [Google Scholar] [CrossRef]
  28. Yigit, S. A machine-learning-based method for thermal design optimization of residential buildings in highly urbanized areas of Turkey. J. Build. Eng. 2021, 38, 102225. [Google Scholar] [CrossRef]
  29. Zhao, S. Using artificial neural network and WebGL to algorithmically optimize window wall ratios of high-rise office buildings. J. Comput. Des. Eng. 2021, 8, 638–653. [Google Scholar] [CrossRef]
  30. Bambara, J.; Athienitis, A.K.; Eicker, U. Residential Densification for Positive Energy Districts. Front. Sustain. Cities 2021, 3, 630973. [Google Scholar] [CrossRef]
  31. Fong, K.; Lee, C.K. Towards net zero energy design for low-rise residential buildings in subtropical Hong Kong. Appl. Energy 2012, 93, 686–694. [Google Scholar] [CrossRef]
  32. Rehman, H.U.; Hirvonen, J.; Jokisalo, J.; Kosonen, R.; Sirén, K. EU emission targets of 2050: Costs and CO2 emissions comparison of three different solar and heat pump-based community-level district heating systems in nordic conditions. Energies 2020, 13, 4167. [Google Scholar] [CrossRef]
  33. Battaglia, V.; Massarotti, N.; Vanoli, L. Urban regeneration plans: Bridging the gap between planning and design energy districts. Energy 2022, 254, 124239. [Google Scholar] [CrossRef]
  34. Sougkakis, V.; Lymperopoulos, K.; Nikolopoulos, N.; Margaritis, N.; Giourka, P.; Angelakoglou, K. An investigation on the feasibility of near-zero and positive energy communities in the Greek context. Smart Cities 2020, 3, 362–384. [Google Scholar] [CrossRef]
  35. Stermieri, L.; Delmastro, C.; Becchio, C.; Corgnati, S.P. Linking Dynamic Building Simulation with Long-Term Energy System Planning to Improve Buildings Urban Energy Planning Strategies. Smart Cities 2020, 3, 1242–1265. [Google Scholar] [CrossRef]
  36. Lu, Y.; Li, P.; Lee, Y.P.; Song, X. An integrated decision-making framework for existing building retrofits based on energy simulation and cost-benefit analysis. J. Build. Eng. 2021, 43, 103200. [Google Scholar] [CrossRef]
  37. Mutani, G.; Usta, Y. Design and Modeling Renewable Energy Communities: A Case Study in Cagliari (Italy). Int. J. Sustain. Dev. Plann. 2022, 17, 1041–1051. [Google Scholar] [CrossRef]
  38. Hirvonen, J.; Heljo, J.; Jokisalo, J.; Kurvinen, A.; Saari, A.; Niemelä, T.; Sankelo, P.; Kosonen, R. Emissions and power demand in optimal energy retrofit scenarios of the Finnish building stock by 2050. Sustain. Cities Soc. 2021, 70, 102896. [Google Scholar] [CrossRef]
  39. Asaee, S.R.; Ugursal, V.I.; Beausoleil-Morrison, I. Development and analysis of strategies to facilitate the conversion of Canadian houses into net zero energy buildings. Energy Policy 2019, 126, 118–130. [Google Scholar] [CrossRef]
  40. Oberegger, U.F.; Pernetti, R.; Lollini, R. Bottom-up building stock retrofit based on levelized cost of saved energy. Energy Build. 2020, 210, 109757. [Google Scholar] [CrossRef]
  41. Omar, A.I.; Khattab, N.M.; Aleem, S.H.A. Optimal strategy for transition into net-zero energy in educational buildings: A case study in El-Shorouk City, Egypt. Sustain. Energy Technol. Assess. 2022, 49, 101701. [Google Scholar] [CrossRef]
  42. Shen, P.; Braham, W.; Yi, Y.; Eaton, E. Rapid multi-objective optimization with multi-year future weather condition and decision-making support for building retrofit. Energy 2019, 172, 892–912. [Google Scholar] [CrossRef]
  43. Leal, V.M.S.; Granadeiro, V.; Azevedo, I.; Boemi, S.-N. Energy and economic analysis of building retrofit and energy offset scenarios for Net Zero Energy Buildings. Adv. Build. Energy Res. 2015, 9, 120–139. [Google Scholar] [CrossRef]
  44. Murray, S.N.; Walsh, B.P.; Kelliher, D.; O’Sullivan, D. Multi-variable optimization of thermal energy efficiency retrofitting of buildings using static modelling and genetic algorithms–A case study. Build Environ. 2014, 75, 98–107. [Google Scholar] [CrossRef]
  45. Saretta, E.; Caputo, P.; Frontini, F. An integrated 3D GIS-based method for estimating the urban potential of BIPV retrofit of façades. Sustain. Cities Soc. 2020, 62, 102410. [Google Scholar] [CrossRef]
  46. Groppi, D.; de Santoli, L.; Cumo, F.; Garcia, D.A. A GIS-based model to assess buildings energy consumption and usable solar energy potential in urban areas. Sustain. Cities Soc. 2018, 40, 546–558. [Google Scholar] [CrossRef]
  47. Gupta, R.; Gregg, M. Targeting and modelling urban energy retrofits using a city-scale energy mapping approach. J. Clean. Prod. 2018, 174, 401–412. [Google Scholar] [CrossRef]
  48. Jokinen, I.; Lund, A.; Hirvonen, J.; Jokisalo, J.; Kosonen, R.; Lehtonen, M. Coupling of the electricity and district heat generation sectors with building stock energy retrofits as a measure to reduce carbon emissions. Energy Convers. Manag. 2022, 269, 115961. [Google Scholar] [CrossRef]
  49. Pavičević, M.; Novosel, T.; Pukšec, T.; Duić, N. Hourly optimization and sizing of district heating systems considering building refurbishment–Case study for the city of Zagreb. Energy 2017, 137, 1264–1276. [Google Scholar] [CrossRef]
  50. Zheng, D.; Yu, L.; Wang, L.; Tao, J. Integrating willingness analysis into investment prediction model for large scale building energy saving retrofit: Using fuzzy multiple attribute decision making method with Monte Carlo simulation. Sustain. Cities Soc. 2019, 44, 291–309. [Google Scholar] [CrossRef]
  51. Woo, J.-H.; Menassa, C. Virtual retrofit model for aging commercial buildings in a smart grid environment. Energy Build. 2014, 80, 424–435. [Google Scholar] [CrossRef]
  52. Hsueh, S.-L.; Feng, Y.; Sun, Y.; Jia, R.; Yan, M.-R. Using AI-MCDM Model to Boost Sustainable Energy System Development: A Case Study on Solar Energy and Rainwater Collection in Guangdong Province. Sustainability 2021, 13, 12505. [Google Scholar] [CrossRef]
  53. Fard, F.A.; Nasiri, F. A bi-objective optimization approach for selection of passive energy alternatives in retrofit projects under cost uncertainty. Energy Built Environ. 2020, 1, 77–86. [Google Scholar] [CrossRef]
  54. Wang, Z.; Liu, Q.; Zhang, B. What kinds of building energy-saving retrofit projects should be preferred? Efficiency evaluation with three-stage data envelopment analysis (DEA). Renew. Sustain. Energy Rev. 2022, 161, 112392. [Google Scholar] [CrossRef]
  55. Lombardi, P.; Abastante, F.; Torabi Moghadam, S.; Toniolo, J. Multicriteria Spatial Decision Support Systems for Future Urban Energy Retrofitting Scenarios. Sustainability 2017, 9, 1252. [Google Scholar] [CrossRef]
  56. Carli, R.; Dotoli, M.; Pellegrino, R.; Ranieri, L. A decision making technique to optimize a buildings’ stock energy efficiency. IEEE Trans. Syst. Man Cybern. Syst. 2016, 47, 794–807. [Google Scholar] [CrossRef]
  57. Deb, C.; Dai, Z.; Schlueter, A. A machine learning-based framework for cost-optimal building retrofit. Appl. Energy 2021, 294, 116990. [Google Scholar] [CrossRef]
  58. Benzar, B.-E.; Park, M.; Lee, H.-S.; Yoon, I.; Cho, J. Determining retrofit technologies for building energy performance. J. Asian Archit. Build. Eng. 2020, 19, 367–383. [Google Scholar] [CrossRef]
  59. Liu, B.; Pouramini, S. Multi-objective optimization for thermal comfort enhancement and greenhouse gas emission reduction in residential buildings applying retrofitting measures by an Enhanced Water Strider Optimization Algorithm: A case study. Energy Rep. 2021, 7, 1915–1929. [Google Scholar] [CrossRef]
  60. Luo, X.; Oyedele, L.O. Integrated life-cycle optimisation and supply-side management for building retrofitting. Renew. Sustain. Energy Rev. 2022, 154, 111827. [Google Scholar] [CrossRef]
  61. Keirstead, J.; Calderon, C. Capturing spatial effects, technology interactions, and uncertainty in urban energy and carbon models: Retrofitting newcastle as a case-study. Energy Policy 2012, 46, 253–267. [Google Scholar] [CrossRef]
  62. Rinaldi, A.; Yilmaz, S.; Patel, M.K.; Parra, D. What adds more flexibility? An energy system analysis of storage, demand-side response, heating electrification, and distribution reinforcement. Renew. Sustain. Energy Rev. 2022, 167, 112696. [Google Scholar] [CrossRef]
  63. Luo, X.; Oyedele, L.O. A data-driven life-cycle optimisation approach for building retrofitting: A comprehensive assessment on economy, energy and environment. J. Build. Eng. 2021, 43, 102934. [Google Scholar] [CrossRef]
  64. Zheng, D.; Yu, L.; Wang, L. A techno-economic-risk decision-making methodology for large-scale building energy efficiency retrofit using Monte Carlo simulation. Energy 2019, 189, 116169. [Google Scholar] [CrossRef]
  65. Syal, M.; Duah, D.; Samuel, S.; Mazor, M.; Mo, Y.; Cyr, T. Information framework for intelligent decision support system for home energy retrofits. J. Constr. Eng. Manag. 2014, 140, 04013030. [Google Scholar] [CrossRef]
  66. González, A.G.; Zotano, M.Á.G.; Swan, W.; Bouillard, P.; Elkadi, H. Maturity Matrix Assessment: Evaluation of Energy Efficiency Strategies in Brussels Historic Residential Stock. Energy Procedia 2017, 111, 407–416. [Google Scholar] [CrossRef]
  67. Wang, Q.; Laurenti, R.; Holmberg, S. A novel hybrid methodology to evaluate sustainable retrofitting in existing Swedish residential buildings. Sustain. Cities Soc. 2015, 16, 24–38. [Google Scholar] [CrossRef]
  68. Stanica, D.-I.; Karasu, A.; Brandt, D.; Kriegel, M.; Brandt, S.; Steffan, C. A methodology to support the decision-making process for energy retrofitting at district scale. Energy Build. 2021, 238, 110842. [Google Scholar] [CrossRef]
  69. Carli, R.; Dotoli, M.; Pellegrino, R. A hierarchical decision-making strategy for the energy management of smart cities. IEEE Trans. Autom. Sci. Eng. 2016, 14, 505–523. [Google Scholar] [CrossRef]
  70. Dadar, S.; Đurin, B.; Alamatian, E.; Plantak, L. Impact of the Pumping Regime on Electricity Cost Savings in Urban Water Supply System. Water 2021, 13, 1141. [Google Scholar] [CrossRef]
  71. Kotulski, L.; Basiura, A.; Wojnicki, I.; Siuchta, S. Lighting System Modernization as a Source of Green Energy. Energies 2021, 14, 2771. [Google Scholar] [CrossRef]
  72. Carli, R.; Dotoli, M. A Dynamic Programming Approach for the Decentralized Control of Energy Retrofit in Large-Scale Street Lighting Systems. IEEE Trans. Autom. Sci. Eng. 2020, 17, 1140–1157. [Google Scholar] [CrossRef]
  73. Carli, R.; Dotoli, M.; Cianci, E. An optimization tool for energy efficiency of street lighting systems in smart cities. IFAC-PapersOnLine 2017, 50, 14460–14464. [Google Scholar] [CrossRef]
  74. Bojun, W.; Xiaojun, L.; Yanping, Y. Energy efficiency retrofitting of lighting in unversity libraries based on illumination suitability analysis. Light Eng. 2018, 26, 132–139. [Google Scholar]
  75. Gonçalves, D.; Sheikhnejad, Y.; Oliveira, M.; Martins, N. One step forward toward smart city Utopia: Smart building energy management based on adaptive surrogate modelling. Energy Build. 2020, 223, 110146. [Google Scholar] [CrossRef]
  76. Ochoa, C.E.; Capeluto, I.G. Decision methodology for the development of an expert system applied in an adaptable energy retrofit façade system for residential buildings. Renew. Energy 2015, 78, 498–508. [Google Scholar] [CrossRef]
  77. Becchio, C.; Bottero, M.C.; Corgnati, S.P.; Dell’Anna, F. Decision making for sustainable urban energy planning: An integrated evaluation framework of alternative solutions for a NZED (Net Zero-Energy District) in Turin. Land Use Policy 2018, 78, 803–817. [Google Scholar] [CrossRef]
  78. Dirutigliano, D.; Delmastro, C.; Moghadam, S.T. Energy efficient urban districts: A multi-criteria application for selecting retrofit actions. Int. J. Heat Technol. 2017, 35, S49–S57. [Google Scholar] [CrossRef]
  79. Hirvonen, J.; Saari, A.; Jokisalo, J.; Kosonen, R. Socio-economic impacts of large-scale deep energy retrofits in Finnish apartment buildings. J. Clean. Prod. 2022, 368, 133187. [Google Scholar] [CrossRef]
  80. Mata, É.; Wanemark, J.; Österbring, M.; Shadram, F. Ambition meets reality–Modeling renovations of the stock of apartments in Gothenburg by 2050. Energy Build. 2020, 223, 110098. [Google Scholar] [CrossRef]
  81. Wang, B.; Xia, X.; Cheng, Z.; Liu, L. Optimal maintenance planning in building retrofitting with interacting energy effects. Optim. Control Appl. Methods 2020, 41, 2023–2036. [Google Scholar] [CrossRef]
  82. Skiba, M.; Mrówczyńska, M.; Sztubecka, M.; Bazan-Krzywoszańska, A.; Kazak, J.K.; Leśniak, A.; Janowiec, F. Probability estimation of the city’s energy efficiency improvement as a result of using the phase change materials in heating networks. Energy 2021, 228, 120549. [Google Scholar] [CrossRef]
  83. Asaee, S.R.; Ugursal, V.I.; Beausoleil-Morrison, I. Techno-economic feasibility evaluation of air to water heat pump retrofit in the Canadian housing stock. Appl. Therm. Eng. 2017, 111, 936–949. [Google Scholar] [CrossRef]
  84. Meek, A.; Jayasuriya, N.; Horan, E.; Adams, R. Environmental Benefits of Retrofitting Green Roofs to a City Block. J. Hydrol. Eng. 2014, 20, 05014020. [Google Scholar] [CrossRef]
  85. Walter, T.; Sohn, M.D. A regression-based approach to estimating retrofit savings using the Building Performance Database. Appl. Energy 2016, 179, 996–1005. [Google Scholar] [CrossRef]
  86. Jahani, E.; Cetin, K. Energy savings and retrofit assessment for city-scale residential building stock during extreme heatwave events using genetic algorithm-numerical moment matching. Clean Technol. Environ. Policy 2022, 24, 2081–2098. [Google Scholar] [CrossRef]
  87. Capeluto, I.G.; Ben-Avraham, O. Assessing the green potential of existing buildings towards smart cities and districts. Indoor Built Environ. 2016, 25, 1124–1135. [Google Scholar] [CrossRef]
  88. Ruparathna, R.; Hewage, K.; Sadiq, R. Economic evaluation of building energy retrofits: A fuzzy based approach. Energy Build. 2017, 139, 395–406. [Google Scholar] [CrossRef]
  89. Luo, X.; Oyedele, L.O.; Owolabi, H.A.; Bilal, M.; Ajayi, A.O.; Akinade, O.O. Life cycle assessment approach for renewable multi-energy system: A comprehensive analysis. Energy Convers. Manag. 2020, 224, 113354. [Google Scholar] [CrossRef]
  90. Moghadam, S.T.; Lombardi, P. An interactive multi-criteria spatial decision support system for energy retrofitting of building stocks using CommuntiyVIZ to support urban energy planning. Build Environ. 2019, 163, 106233. [Google Scholar] [CrossRef]
  91. Buffat, R.; Schmid, L.; Heeren, N.; Froemelt, A.; Raubal, M.; Hellweg, S. GIS-based decision support system for building retrofit. Energy Procedia 2017, 122, 403–408. [Google Scholar] [CrossRef]
  92. Madrazo, L.; Sicilia, A.; Massetti, M.; Plazas, F.L.; Ortet, E. Enhancing energy performance certificates with energy related data to support decision making for building retrofitting. Therm. Sci. 2018, 22, 957–969. [Google Scholar] [CrossRef]
  93. Broers, W.M.H.; Vasseur, V.; Kemp, R.; Abujidi, N.; Vroon, Z.A.E.P. Decided or divided? An empirical analysis of the decision-making process of Dutch homeowners for energy renovation measures. Energy Res. Soc. Sci. 2019, 58, 101284. [Google Scholar] [CrossRef]
  94. Tajani, F.; Morano, P.; Di Liddo, F.; Doko, E. A Model for the Assessment of the Economic Benefits Associated with Energy Retrofit Interventions: An Application to Existing Buildings in the Italian Territory. Appl. Sci. 2022, 12, 3385. [Google Scholar] [CrossRef]
  95. Pardo-Bosch, F.; Cervera, C.; Ysa, T. Key aspects of building retrofitting: Strategizing sustainable cities. J. Environ. Manag. 2019, 248, 109247. [Google Scholar] [CrossRef] [PubMed]
  96. Bisello, A. Assessing Multiple Benefits of Housing Regeneration and Smart City Development: The European Project SINFONIA. Sustainability 2020, 12, 8038. [Google Scholar] [CrossRef]
  97. Jankovic, L. Lessons learnt from design, off-site construction and performance analysis of deep energy retrofit of residential buildings. Energy Build. 2019, 186, 319–338. [Google Scholar] [CrossRef]
Buildings 13 01425 g001 550
Figure 1. Number of publications by year.
Figure 1. Number of publications by year.
Buildings 13 01425 g001
Buildings 13 01425 g002 550
Figure 2. Distribution of research articles by the country of affiliation (Round-off error may occur).
Figure 2. Distribution of research articles by the country of affiliation (Round-off error may occur).
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Buildings 13 01425 g003 550
Figure 3. Distribution of research articles by the research field.
Figure 3. Distribution of research articles by the research field.
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Table
Table 1. List of journals with the largest number of articles by the researched topic.
Table 1. List of journals with the largest number of articles by the researched topic.
JournalNumber of Papers
Energy and Buildings15
Energy6
Applied Energy5
Sustainable Cities and Society5
Energies4
Journal of Building Engineering4
Sustainability4
Energy Conversion and Management3
Journal of Cleaner Production3
Renewable and Sustainable Energy Reviews3
Smart Cities3
Table
Table 2. Summary of all research approaches of reviewed papers.
Table 2. Summary of all research approaches of reviewed papers.
ApproachTechniqueExampleReferenceRemark
Energy simulator-based approachEnergy simulation softwareEnergyPlus[7,8,9]The simulation software based on scientific principles is trustworthy. However, it requires detailed input data that may not easily be available.
EnergyPlus + DesignBuilder [10,11,12,13,14,15,16,17,18,19,20,21]
EnergyPlus + Rhino + Grasshopper[22,23]
EnergyPlus + MATLAB[10,11,17,18,19,24,25,26]
EnergyPlus + Python[26,27,28,29]
TRNSYS[30,31,32,33]
EnergyPLAN[34,35]
IES-VE[36,37]
IDA ICE[38]
ESP-r[39]
PHPP[40]
HOMER[41]
Self-developed simulation methodThermal balance-based building energy simulation [42,43,44]The self-developed simulation method is flexible to different customized requirements.
Geographic information system (GIS)-based solar energy simulation[45,46,47]
Optimization modeling-based approachIndustrial and Systems Engineering (ISE) modelMixed-Integer Linear Programming (MILP) model[48,49]ISE models focus on the sociotechnical balance of different urban energy systems.
Analytical Hierarchy Process (AHP) model[50,51,52]
Analytical Network Process (ANP) model[53]
Data Envelopment Analysis (DEA) model[54]
Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) and the “Playing Cards” method[55]
Weighted Sum Model (WSM)[56]
Recurrent Neural Network (RNN) model[57]
Quality Function Deployment (QFD) framework[58]
Enhanced Water Strider Optimization (EWSO) model[59]
Ant Colony Optimization (ACO) model[60]
Technologies and Urban Resource Networks (TURN) model[61]
Open-source sector coupling model (GRIMSEL-FLEX)[62]
Data-driven life-cycle optimization model[63]
Techno-economic-risk decision-making methodology (TERDMM) model[64]
Information model[65]
Maturity matrix assessment model[66]
Hybrid decision-making workflow[67,68]
Mechanical Engineering (ME) modelHierarchical decision-making model for urban energy management[69]ME models focus on the technological advancement of specific energy systems.
Genetic Algorithm (GA) for the optimization of pumping system operation[70]
Decision-making model for street lighting control[71,72,73]
Decision-making model for indoor lighting control [74]
Intelligent supervisory predictive control model for heating, ventilation, and air conditioning (HVAC) system[75]
Expert system for an adaptable energy retrofit façade system[76]
Social, Behavioral, and Economic sciences (SBE) modelDecision-making model considering socio-economic issues[77,78,79]SBE models focus on the social benefits of the urban retrofit plan.
Decision-making model considering demographic dynamics[80,81]
Assessment-based approachData analyticsRetrofit solution assessment[82,83,84]Assessment-based decision-making can be applied to various scales if the data for different scales is available, from individual buildings to entire urban areas.
Energy saving prediction[85,86,87]
Life cycle assessment[88,89]
System integration-based approachGIS GIS-based urban building retrofit platform[90,91,92]Integrated systems can provide user-friendly interfaces for decision-making.
Empirical study-based approachCase studyLessons from completed and ongoing energy retrofit project [93,94,95,96,97]Empirical study can reveal unexpected challenges that may not be apparent in simulations and offer insights into the engagement and collaboration of stakeholders.
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Shu, L.; Zhao, D. Decision-Making Approach to Urban Energy Retrofit—A Comprehensive Review. Buildings 2023, 13, 1425. https://doi.org/10.3390/buildings13061425

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Shu L, Zhao D. Decision-Making Approach to Urban Energy Retrofit—A Comprehensive Review. Buildings. 2023; 13(6):1425. https://doi.org/10.3390/buildings13061425

Chicago/Turabian Style

Shu, Lei, and Dong Zhao. 2023. "Decision-Making Approach to Urban Energy Retrofit—A Comprehensive Review" Buildings 13, no. 6: 1425. https://doi.org/10.3390/buildings13061425

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