Hygrothermal calibration and validation of vernacular dwellings: A genetic algorithm-based optimisation methodology. (1st September 2022)

• Record Type: Journal Article
• Authors: Costa-Carrapiço, Inês; Croxford, Ben; Raslan, Rokia; Neila González, Javier
• Is Part Of: Journal of building engineering. Volume 55(2022)
• Publication Date: 2022-09-01
• Subjects: Simulation model calibration -- Hygrothermal model validation -- Vernacular dwellings -- Genetic algorithm -- Multi-objective optimisation; CoP Coefficient of performance -- CP Compromise Programming -- (CV)RMSE Coefficient of Variation of the Root-Mean-Square Error -- GA Genetic Algorithm -- IAQ Indoor Air Quality -- LV1/2 Level 1/2 -- MAE Mean Absolute Error -- MBE Mean Bias Error -- MCDM Multi-criteria decision-making -- MOEAs multi-objective evolutionary algorithms -- MOO Multi-objective optimisation -- NMBE Normalised Mean Bias Error -- NSGA-II Non-dominated Sorting Genetic Algorithm -- RH Relative Humidity -- RMSE Root Mean Square Error -- SA Sensitivity Analysis -- SVV São Vicente e Ventosa -- Ta Air temperature -- Tg Globe Temperature -- Tmrt Mean Radiant Temperature -- Ts Surface -- Va Air velocity; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2022.104717
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: Heritage model calibration and validation are crucial for decreasing uncertainty and enhancing the robustness of simulation results and conservation interventions. Yet, hygrothermal modelling methodologies are marked by significant heterogeneity and lack of robustness. Aiming to provide a solution for the drawbacks identified, this study puts forth a comprehensive hygrothermal modelling methodology. Following the in situ data collection of a subset of heritage buildings, 22 vernacular dwellings in Southern Portugal, a three-step method was developed, consisting of: Morris sensitivity analysis, optimisation-based calibration, and validation and multi-criteria decision-making (MCDM). A genetic algorithm multi-objective optimisation-based calibration with NSGA-II was implemented for simultaneously minimising the statistical indicators RMSE and MAE for the indoor air temperature of the winter and summer models. The validation and MCDM were conducted by means of threshold compliance and Compromise Programming. NSGA-II found Pareto frontiers composed of nine and six optimal solutions for the summer and winter models, respectively, in nearly 3 h each. All optimal solutions significantly decreased the RMSE and MAE, especially in the summer model, regarding the baseline data. The final solutions selected after the MCDM resulted in an accuracy improvement of 51% and 54% for the RMSE and MAE for the winter model and 80% and 81% for the RMSE and MAE in the summer model, compared to the baseline models. The strong correlation found between the calibrated models and the measured data along with the enhancement of calibrated data regarding the baseline model, highlighted the potential of using GAs to obtain calibrated vernacular models that robustly predict real building performance and foster better retrofitting decision-making. Highlights: Calibrated heritage models are key for robust simulation results and conservation interventions. GA multi-objective optimisation-based calibration methodology was developed and applied to vernacular dwellings. Three-step method: Morris SA, optimisation-based calibration, and validation and MCDM. All optimal solutions found significantly decreased the error indexes, especially in summer. GAs' potential for calibrating vernacular models for robust performance prediction highlighted.