A Review of Data-Driven Methods in Building Retrofit and Performance Optimization: From the Perspective of Carbon Emission Reductions
Abstract
:1. Introduction
1.1. Background
1.2. Previous Reviews
1.2.1. A Review of Building Retrofit Research
1.2.2. A Review of Building CER Retrofit Research
1.2.3. A Review of the Application of Data-Driven Methods in Building-Performance Analyses
1.3. Outline and Structure of This Review
2. Literature Screening and Bibliometric Analysis
2.1. Literature Search and Screening
- To ensure timeliness, articles published within the last 20 years (2003–2023) were selected. Journal articles, known for their rigorous peer-review process [36], were prioritized for their representation and impact in this field [37]. Conference papers, dissertations, and non-English language publications were excluded, retaining only English-language journal articles. Additionally, to facilitate the focus on research methodologies and processes, review papers were omitted from consideration.
- The literature reviewed addresses CEs during the operational phase of buildings, covering life-cycle carbon emissions (LCCEs), the GWP, and building environmental effects. Studies focusing solely on energy consumption (EC) without converting it to CE objectives were excluded due to differences in concepts and calculation methodologies. Similarly, the literature exclusively discussing CEs during building construction or other phases was also omitted from consideration.
- The research must incorporate one or more data-driven methods, such as statistical analyses, optimization algorithms, or machine learning (ML) to optimize building performance. Studies that merely listed and compared retrofit plans without employing these methods were excluded.
- The literature must address the impacts of envelope retrofit measures on the overall building performance. Studies exclusively focusing on the operations of building mechanical systems, energy structure predictions, and similar topics were excluded. Additionally, articles concentrating solely on specific local building components like curtain wall retrofits and structural seismic performance optimizations were also excluded.
2.2. Literature Statistics and Bibliometric Analysis
2.3. General Process of BPO Based on Data-Driven Methods
3. The Construction BPO Models
3.1. Optimization Model Based on Physical Simulation Methods
3.1.1. BPS Tools
3.1.2. Model Calibration
3.2. Surrogate and Mathematical Models
- Continuous and discrete variables are distinguished, and both input and output variables are standardized and normalized to ensure a comparability of the data. Typically, the correctly formatted data are divided into training and testing sets in an 80%-to-20% ratio [115], after which an appropriate mathematical model is selected [116].
- The model is trained, and to prevent overfitting (where the model performs well on the training datasets but struggles with out-of-sample data), hyperparameter optimization is needed to balance variances and biases. When choosing hyperparameters, strategies like a grid search can be employed [117], with cross-validations serving as the scoring method [118].
- The model is validated, and various metrics are chosen to assess its accuracy. Common evaluation indicators include the mean absolute percent error (MAPE), mean absolute error (MAE), and CV (RMSE) [119], with CV (RMSE) being particularly favored, due to its ability to provide a unitless measurement, which facilitates straightforward comparisons of indicators [120].
3.3. Optimization Objectives and Parameters
3.3.1. Building-Performance Indicators
3.3.2. Optimization Parameters and Variables
3.3.3. Constraints on Optimization Objectives and Parameters
4. Optimization and Decision-Making Process
4.1. Optimization Process Based on Data-Driven Methods
4.2. Solution Set Evaluation and Decision-Making Methods
5. Discussions
5.1. The Research Status Quo
5.2. Optimization and Surrogate Models
5.3. Optimization Methods and Tools
5.4. Future Work
6. Conclusions
- There are usually two workflows to optimize the building performance. One is the workflow of the optimization of the physical simulation (model surrogate) performance: Using the combined input of a building site and energy-carbon-related retrofit variables, a BPO process based on a physical simulation is established. The generated datasets can be either iteratively processed with optimization algorithms directly or trained as a surrogate model, validated, and then processed using the MOO method. The other is the workflow of mathematical modeling-optimization analyses: with sufficient actual field-measured empirical data available, data-driven methods, such as regression or machine learning, are used to develop mathematical models, and multiple objectives are comprehensively optimized from the perspective of building CERs.
- A building retrofit aims to maximize its benefits by integrating environmental, economic, and social considerations. Therefore, alongside CE objectives, factors like costs and thermal comfort should also be taken into account. There are 27 relevant studies in Table 2 related to the comprehensive optimization of three or more objectives, accounting for 60% of the total. Discussions on retrofit parameters should extensively cover aspects such as the thermal performance of the building envelope, building equipment and energy systems, and the utilization of renewable energy sources.
- Data-driven methods applied in optimization enable the sampling, screening, and iterative refinement of retrofit plans using computational tools, facilitating the determination of optimal solutions. The advancement and deployment of surrogate models make simplified mathematical calculations replace complex physical simulations, which further enhance optimization efficiency while ensuring accuracy.
- In the reviewed studies, only 2.2% (1 article) and 6.7% (3 articles) of the total focus on the impacts of human behaviors and climate change on building retrofits, respectively. Future research should delve deeper into the application of data-driven methods in building CER retrofits and BPO, considering user behaviors and variations in retrofit conditions amid long-term climate change scenarios. In addition, more work is needed to improve the accuracy of surrogate models and enhance generalizations and transfer capabilities.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AC | Annual cost |
ACO | Ant colony optimization |
AHP | Analytic hierarchy process |
ANN | Artificial neural network |
ASHRAE | American Society of Heating, Refrigerating, and Air-Conditioning Engineers |
BPO | Building performance optimization |
BPS | Building performance simulation |
CEs | Carbon emissions |
CER | Carbon emission reduction |
CV(RMSE) | Coefficient of variation of root mean square error |
DNN | Deep neural network |
DOEs | Design of experiments |
EA | Evolutionary algorithm |
EC | Energy consumption |
ECEs | Embodied carbon emissions |
EWSOA | Enhanced water strider optimization algorithm |
FEMP | Federal Energy Management Program |
GA | Genetic algorithm |
GBRT | Gradient boosting regression tree |
GC | Global cost |
GHGEs | Greenhouse gas emissions |
GSA | Global sensitivity analysis |
GWP | Global warming potential |
IPMVP | International Performance Measurement and Verification Protocol |
LCA | Life-cycle assessment |
LCC | Life-cycle cost |
LCCE | Life-cycle carbon emission |
LCE | Life-cycle energy consumption |
LSA | Local sensitivity analysis |
MAE | Mean absolute error |
MAPE | Mean absolute percent error |
MARSs | Multiple adaptive regression splines |
MBE | Mean bias error |
MILP | Mixed-integer linear programming |
MOEA/D | Multi-objective evolutionary algorithm based on decomposition |
MOGA | Multi-objective genetic algorithm |
MOO | Multi-objective optimization |
MVLR | Multi-variate linear regression |
NOP | Nonlinear optimization programming |
NSGA-II/III | Non-dominated sorting genetic algorithm II/III |
a/pNSGA-II | Active/passive archive NSGA-II |
prNSGA-III | NSGA-III algorithm augmented by parallel computing structure and result-saving archive |
OC | Operational cost |
OCES | Operational carbon emissions |
PRISMA | Preferred reporting items for systemic reviews and meta-analyses |
PSO | Particle swarm optimization |
RC | Retrofit cost |
SEGA | Strengthen elitist genetic algorithm |
SPEA2 | Strength Pareto evolutionary algorithm2 |
SQOL | Social quality of life |
TDHS | Thermal discomfort hours |
WC | Water consumption |
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Term | Keywords |
---|---|
Term 1 | “building renovation” OR “building reconstruction” OR “building retrofit *” OR “building refurbishment” OR “building repairment” OR “building restoration” OR “building upgrade” OR “building renewal” OR “building improvement” OR “building reformation” |
Term 2 | multi-objective OR multi-criteria OR optimization |
Term 3 | “carbon emission” OR “carbon mitigation” OR “CO2 emission” OR “CO2 mitigation” OR “greenhouse gas” OR “global warming” OR “environmental impact” OR “sustainable development” |
Refs. | Location | Building Type | Optimization Objective | Optimization Variable |
---|---|---|---|---|
[23] | UK | Office building | LCCs, LCEs, and LCCEs | Insulation material area of roof and exterior wall, equipment and energy system, PV panel area, and solar thermal device. |
[24] | Switzerland | Residential building | CEs and ACs | U-/R-value of roof, exterior wall, and ground; window type; equipment and energy system; PV system; and solar thermal device. |
[25] | Italy | Office building | ECs, TDHs, GCs, and GHGEs | Surface material characteristics of roof and exterior wall, insulation material thickness of roof and exterior wall, window type, equipment and energy system, sunshade component, and PV panel angle and area. |
[38] | UK | Office building | LCCEs and OCE | Insulation material type of roof and exterior wall, equipment and energy system, and solar thermal device. |
[39] | UK | Office building | LCCs, LCEs, and LCCEs | Insulation material type and area of roof and exterior wall, window-to-wall ratio, equipment and energy system, PV panel area, and solar thermal device. |
[40] | China | Shopping mall | OCEs | U/R-value of exterior wall, Glass material, Sunshade component and equipment and energy system. |
[41] | Iran | Residential building | CEs and TDHs | Insulation material thickness of roof and exterior wall, insulation material thickness and type of ground, window type, airtightness, and equipment and energy system. |
[42] | Canada | Office building | ECs and CEs | Insulation material type of roof, exterior wall, and floor; window type; airtightness; and equipment and energy system. |
[43] | Finland | Office building | LCCs, RCs, CEs, and TDHs | Insulation material thickness of roof and exterior wall, window type, sunshade component, equipment and energy system, and PV system. |
[44] | Iran | Residential building | ECs and the GWP | Insulation material type and thickness of exterior wall and exterior wall type (combination of different materials). |
[45] | Canada | Educational building | ECs, LCCs, and LCAs | Type of roof and exterior wall, glass material, airtightness, window opening percentage, and equipment and energy system. |
[46] | Korea | Residential building | RCs, LCCs, LCCEs, and CERs | Insulation material type and thickness of exterior wall, window type, sunshade component, and equipment and energy system. |
[47] | France | Educational building | ECs, TDHs, RCs, and CEs | Type of roof, floor, ground, and interior and exterior wall; window type, and sunshade component. |
[48] | Europe | Residential building | ECs, RCs, OCs, and CEs | Surface material characteristics of roof and exterior wall, window type, sunshade component, sunspace, building form, PV panel angle and area, and solar thermal device. |
[49] | Finland | Residential building | ECs, LCCs, and CEs | Insulation material thickness of roof and exterior wall, window type, door material, PV panel area, solar thermal device, and equipment and energy system. |
[50] | Iran | Residential building | CEs, WCs, LCCs, and TDHs | Insulation material type and thickness of roof and exterior wall, glass material, filling gas, PV panel area, and equipment and energy system. |
[51] | China | Residential building | CEs, TDHs, and GCs | Surface material characteristics, insulation material type and thickness of roof and exterior wall, window type, sunshade component, sunspace, and PV panel angle and area. |
[52] | China | Residential building | ECs, RCs, and CERs | Insulation material type of roof, exterior wall and floor, glass material, window-to-wall ratio, and sunspace. |
[53] | Estonia | Residential building | GCs, ECs, and LCCEs | Insulation material thickness of exterior wall, surface material characteristics of roof, window type, door material, and building form. |
[54] | Korea | Educational building | ECs, CEs, RCs, and TDHs | Type of roof, floor, ground, ceiling, and interior and exterior wall; window type; and equipment and energy system. |
[55] | China | Residential building | The GWP, LCCs, and TDHs | Insulation material type and thickness of roof and exterior wall, window type, window-to-wall ratio, and sunshade component. |
[56] | China | Residential building | ECs, LCCEs, and LCCs | Insulation material type and thickness of floor and exterior wall, glass material, window-to-wall ratio, sunshade component, and Airtightness. |
[57] | UK | Residential building | LCCEs and LCCs | Insulation material thickness, exterior wall type, and window-to-wall ratio. |
[58] | Sweden | Residential building | LCEs, LCCEs, and LCCs | Insulation material type and thickness of exterior wall, roof, and ground and window type. |
[59] | Switzerland | Residential building | ACs CEs | U-/R-value of roof, floor, and exterior wall; window type; PV panel area; and solar thermal device, and equipment and energy system. |
[60] | Canada | Residential building | LCCEs and LCCs | Insulation material type of ceiling and exterior wall, window frame material, door material, airtightness, and equipment and energy system. |
[61] | UK | Non-domestic building | BER | Type of roof and exterior wall, window type, and equipment and energy system. |
[62] | Italy | Residential building | ECs, OCs, RCs, and CEs | Insulation material thickness of roof, floor, and exterior wall; surface material characteristics of roof and exterior wall; PV panel angle and area; glass material; sunshade component; building form; sunspace; and solar thermal device. |
[63] | Iran | Residential building | CEs and TDHs | Insulation material thickness of roof, ground, and exterior wall; window type, airtightness, and equipment and energy system. |
[64] | Denmark | Residential building | ECs, the GWP, OCs, and RCs | Insulation material type and thickness of interior wall, insulation material type and thickness of roof and exterior wall, surface material characteristics of roof and exterior wall, window frame material, glass material, PV panel area, solar thermal device, and equipment and energy system. |
[65] | Italy | Residential building | RCs, OCs, ECs, and CEs | Insulation material thickness of roof, floor, and exterior wall; surface material characteristics of exterior wall; PV panel angle and area; sunshade component, building form, sunspace, and solar thermal device. |
[66] | Bosnia and Herzegovina | Residential building | ECs, CEs, and RCs | Insulation material thickness of ceiling and exterior wall, window type, and equipment and energy system. |
[67] | China | Office building | ECs, CEs, and TDHs | PV panel angle and area and equipment and energy system. |
[68] | Switzerland | Residential building | LCCs and GHGEs | Type of roof and exterior wall, window type, airtightness, PV system, solar thermal device, and equipment and energy system. |
[69] | Germany | Residential building | ACs and CEs | Type of roof and exterior wall, window type, PV system, solar thermal device, and equipment and energy system. |
[70] | China | Office building | ECs, CEs, and OCs | U-/R-value of roof and exterior wall, window type, and equipment and energy system. |
[71] | Greece | Residential building | GHGEs and LCCs | Insulation material thickness of roof, ground, and exterior wall; window type, PV system, solar thermal device, and equipment and energy system. |
[72] | China | Office building | LCCEs | Insulation material thickness of roof and exterior wall, surface material characteristics of exterior wall, window type, PV panel area, and equipment and energy system. |
[73] | China | Educational building | ECs and LCCEs | Type of roof, floor, and exterior wall; filling gas; building form; insulation material thickness of floor and exterior wall; window frame material; glass material; building form; insulation material thickness of roof; window-to-wall ratio; sunshade component; PV panel area; and equipment and energy system. |
[74] | USA | Residential building | GHGEs, WCs, the SQOL, and LCCs | U-/R-value of roof, ceiling and exterior wall, glass material, window-to-wall ratio, and equipment and energy system. |
[75] | UK | Residential building | LCCEs and LCCs | Type of roof, floor, ceiling, and interior and exterior wall and window type. |
[76] | Canada | Educational building | ECs, LCCs, and LCAs | Type of roof and exterior wall, glass material, window frame material, window-to-wall ratio, airtightness, window opening percentage, and equipment and energy system. |
[77] | Canada | Office building | ECs, ECEs, and LCCs | Type of roof and exterior wall, glass material, window frame material, window-to-wall ratio, airtightness, sunshade component, and equipment and energy system. |
[78] | China | Office building | ECs, CEs, and LCCs | Insulation material type and thickness of roof and exterior wall and window type. |
[79] | Switzerland | Residential building | LCCs and LCAs | Insulation material type of ceiling and exterior wall; insulation material thickness of ceiling, floor, and exterior wall; glass material; and window frame material. |
Refs. | Location | Building Type | Machine Learning Method (Accuracy) | Sensitivity Analysis Method |
---|---|---|---|---|
[23] | UK | Office building | - | - |
[24] | Switzerland | Residential building | ANN (R2 = 0.94) | - |
[25] | Italy | Office building | - | - |
[38] | UK | Office building | - | - |
[39] | UK | Office building | - | LSA |
[40] | China | Shopping mall | - | LSA |
[41] | Iran | Residential building | - | GSA (DOE) |
[42] | Canada | Office building | - | LSA |
[43] | Finland | Office building | - | - |
[44] | Iran | Residential building | - | - |
[45] | Canada | Educational building | ANN (MSE1 = 0.016 and MSE2 = 0.056) | - |
[46] | Korea | Residential building | - | - |
[47] | France | Educational building | - | - |
[48] | Europe | Residential building | - | - |
[49] | Finland | Residential building | - | - |
[50] | Iran | Residential building | - | - |
[51] | China | Residential building | - | GSA (PCC and SRRC) |
[52] | China | Residential building | - | - |
[53] | Estonia | Residential building | - | - |
[54] | Korea | Educational building | - | - |
[55] | China | Residential building | DNN (R2 > 0.99, CV (RMSE) ≤ 1%, and NMBE ≤ 0.2%) | GSA |
[56] | China | Residential building | - | - |
[57] | UK | Residential building | - | - |
[58] | Sweden | Residential building | - | - |
[59] | Switzerland | Residential building | - | - |
[60] | Canada | Residential building | - | - |
[61] | UK | Non-domestic building | GBRT (RMSE = 1.7%) | LSA |
[62] | Italy | Residential building | - | GSA (SRRC) |
[63] | Iran | Residential building | - | GSA (DOE) |
[64] | Denmark | Residential building | - | - |
[65] | Italy | Residential building | - | GSA (SRRC) |
[66] | Bosnia and Herzegovina | Residential building | - | - |
[67] | China | Office building | - | GSA (SRC) |
[68] | Switzerland | Residential building | - | - |
[69] | Germany | Residential building | - | - |
[70] | China | Office building | - | GSA (Morris) |
[71] | Greece | Residential building | - | - |
[72] | China | Office building | - | - |
[73] | China | Educational building | ANN (MRE = 1.57% R2 = 0.94) | - |
[74] | USA | Residential building | - | - |
[75] | UK | Residential building | - | - |
[76] | Canada | Educational building | - | - |
[77] | Canada | Office building | MVLR and MARSs (MAPE = 0.2–1.8%) | - |
[78] | China | Office building | - | - |
[79] | Switzerland | Residential building | Gaussian process modelling (Kriging) | GSA (Sobol) |
Refs. | Location | Building Type | Optimization Method | Decision-Making Method |
---|---|---|---|---|
[23] | UK | Office building | PSO | - |
[24] | Switzerland | Residential building | MILP | - |
[25] | Italy | Office building | NSGA-II | - |
[38] | UK | Office building | PSO | - |
[39] | UK | Office building | PSO | - |
[40] | China | Shopping mall | Regression | - |
[41] | Iran | Residential building | NSGA-II | - |
[42] | Canada | Office building | - | - |
[43] | Finland | Office building | Pareto-Archive and NSGA-II | - |
[44] | Iran | Residential building | Fitness Comparison | - |
[45] | Canada | Educational building | NSGA-II | - |
[46] | Korea | Residential building | iMOO score | - |
[47] | France | Educational building | NSGA-II | - |
[48] | Europe | Residential building | aNSGA-II and pNSGA-II | Utopia point |
[49] | Finland | Residential building | Pareto-Archive and NSGA-II | - |
[50] | Iran | Residential building | prNSGA-III | TOPSIS |
[51] | China | Residential building | SPEA2 | Utopia point |
[52] | China | Residential building | - | Entropy method (Weight of CERs is 30.95%) |
[53] | Estonia | Residential building | Regression | - |
[54] | Korea | Educational building | NSGA-II/III and MOEA/D | - |
[55] | China | Residential building | NSGA-II | TOPSIS (Weight of the GWP is 37.29%) |
[56] | China | Residential building | NSGA-II | - |
[57] | UK | Residential building | NSGA-II | - |
[58] | Sweden | Residential building | NSGA-II | - |
[59] | Switzerland | Residential building | GA and MILP | - |
[60] | Canada | Residential building | NSGA | - |
[61] | UK | Non-domestic building | GA | - |
[62] | Italy | Residential building | aNSGA-II | Utopia point |
[63] | Iran | Residential building | EWSOA | - |
[64] | Denmark | Residential building | Omni-Optimizer | Utopia point |
[65] | Italy | Residential building | aNSGA-II | Utopia point |
[66] | Bosnia and Herzegovina | Residential building | NSGA-III | Desirability function (Weight of CEs is 30%) |
[67] | China | Office building | NSGA-II | - |
[68] | Switzerland | Residential building | ϵ-constraint | - |
[69] | Germany | Residential building | ϵ-constraint | - |
[70] | China | Office building | - | - |
[71] | Greece | Residential building | MOGA | - |
[72] | China | Office building | NOP and MILP | - |
[73] | China | Educational building | SEGA | - |
[74] | USA | Residential building | GA | - |
[75] | UK | Residential building | NSGA-II | - |
[76] | Canada | Educational building | NSGA-II | - |
[77] | Canada | Office building | - | - |
[78] | China | Office building | AHP | - |
[79] | Switzerland | Residential building | NSGA-II | - |
Simulation Tool | References |
---|---|
Designbuilder | [25,40,41,44,45,46,50,63,66,68,73,76,77,78] |
TRNSYS | [39,47,67,71] |
SIMEB | [42] |
IDA ICE | [43,49,53] |
SketchUp—OpenStudio | [48,52,57,62,65,70] |
Grasshopper—Honeybee | [51,55,56,58,72] |
EnergyPlus | [54,59,74,75] |
HOT2000 | [60] |
Evaluation Indicators | Guideline | Monthly Criteria | Hourly Criteria | References |
---|---|---|---|---|
MBE | ASHRAE | ±5% | ±10% | [46,51,55,56,67] |
IPMVP | - | ±5% | ||
FEMP | ±5% | ±10% | ||
CV (RMSE) | ASHRAE | 15% | 30% | [46,50,51,55,56,67,77] |
IPMVP | - | 20% | ||
FEMP | 15% | 30% |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Luo, S.-L.; Shi, X.; Yang, F. A Review of Data-Driven Methods in Building Retrofit and Performance Optimization: From the Perspective of Carbon Emission Reductions. Energies 2024, 17, 4641. https://doi.org/10.3390/en17184641
Luo S-L, Shi X, Yang F. A Review of Data-Driven Methods in Building Retrofit and Performance Optimization: From the Perspective of Carbon Emission Reductions. Energies. 2024; 17(18):4641. https://doi.org/10.3390/en17184641
Chicago/Turabian StyleLuo, Shu-Long, Xing Shi, and Feng Yang. 2024. "A Review of Data-Driven Methods in Building Retrofit and Performance Optimization: From the Perspective of Carbon Emission Reductions" Energies 17, no. 18: 4641. https://doi.org/10.3390/en17184641
APA StyleLuo, S. -L., Shi, X., & Yang, F. (2024). A Review of Data-Driven Methods in Building Retrofit and Performance Optimization: From the Perspective of Carbon Emission Reductions. Energies, 17(18), 4641. https://doi.org/10.3390/en17184641