Faculty and staff publications

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Browsing Faculty and staff publications by Author "(Leon) Wang, Liangzhu"

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    Calibration of building model based on indoor temperature for overheating assessment using genetic algorithm: Methodology, evaluation criteria, and case study
    (Building and Environment 207 (2022) 108518, 2022) Mutasim Baba, Fuad; Ge, Hua; Zmeureanu, Radu; (Leon) Wang, Liangzhu
    With the increased severity, intensity, and frequency of “heatwaves” due to climate change, it has become imperative to study the overheating risks in existing buildings. To do so, a building simulation model needs to be calibrated based on measured indoor temperatures under the current weather conditions. This paper presents a robust automated methodology that can calibrate a building simulation model based on the indoor hourly temperature in multiple rooms simultaneously with high accuracy. This methodology includes a variance-based sensitivity analysis to determine building parameters that significantly influence indoor air temperatures, the Multi-Objective Genetic Algorithm to calibrate different rooms simultaneously based on the significant param eters identified by the sensitivity analysis, and new evaluation criteria to achieve a high-accuracy calibrated model. Maximum Absolute Difference (MAD), a new metric, that calculates the maximum absolute difference between simulated and measured hourly indoor temperatures, Root Mean Square Error (RMSE), Normalized Mean Bias Error (NMBE) were used as the evaluation criteria. Another new metric is introduced, 1 ◦C Percentage Error criterion that calculates the percentage of the number of hours with an error over 1 ◦C during the cali bration period, to select the best solutions from the Pareto Front solutions. 0.5 ◦C Percentage Error criterion is also used for the level of accuracy the model can achieve. It was found that the calibrated model achieved these metrics with RMSE of 0.3 ◦C, and MAD of 0.8 ◦C, and 85% of data points with an error less than 0.5 ◦C for a school building case.
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    Do high energy-efficient buildings increase overheating risk in cold climates? Causes and mitigation measures required under recent and future climates
    (Elsevier, 2022) Mutasim Baba, Fuad; Ge, Hua; (Leon) Wang, Liangzhu; Zmeureanu, Radu
    Contradictory findings are reported in the literature showing that high energy-efficient buildings have either higher or lower overheating risks compared to old buildings. A methodology is developed using the Global and Local Sensitivity Analysis to quantify the contribution and correlation of individual building envelope parameter to the change in indoor operative temperature. This methodology is applied to an archetype Canadian detached house as a case study to evaluate its overheating risk. The building envelope thermal characteristics studied represent houses built in different periods from 1950 to high energy-efficient buildings in Montreal under different weather generations: typical historical (1961–1990), recent observational (2016), and typical future years 2030 (2026–2045) and 2090 (2080–2099) generated based on RCP-4.5 and 8.5 scenarios. The results showed that the high energy-efficient buildings can be more resilient to climate change than old buildings if adequate ventilation is provided, where the decrease of window and wall U-value, and SHGC all contribute to the decrease in indoor temperature. While without adequate ventilation, the overheating risk in high-energy-efficient buildings can be higher than old buildings, where decreasing wall and window U-values and infiltration rate has a greater contribution to the increase of indoor temperature, while decreasing window SHGC has a lower contribution to the decrease in indoor temperature compared to the case with adequate ventilation. The results also showed that natural ventilation in the high energy-efficient buildings is sufficient to reduce the overheating risk under the current climate but will require additional interior and exterior shading under future climates.
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    Optimizing overheating, lighting, and heating energy performances in Canadian school for climate change adaptation: Sensitivity analysis and multi-objective optimization methodology
    (Building and Environment, 2023) Mutasim Baba, Fuad; Ge, Hua; Zmeureanu, Radu; (Leon) Wang, Liangzhu
    This paper aims to develop long-term adaptation strategies for the existing Canadian school buildings under extreme current and future climates using a developed methodology based on global and local sensitivity analysis and Multi-Objective Optimization Genetic Algorithm. The calibrated simulation model based on indoor and outdoor measured temperature for a school of interest is used to evaluate the optimization strategies. This paper aims to search for the optimum school building design under three simultaneous conflicting objective functions: (1) the minimization of overheating hours to less than 40 h as required by Building Bulletin BB101 building code by using passive mitigation measures, (2) the minimization of heating energy use to less than 15 kW/m2 ac cording to passive house requirements and thus the reduction of greenhouse gas emissions, and (3) the mini mization of artificial lighting energy use to less than the current lighting energy use by maximization of daylighting usage without exceeding acceptable glare index in classrooms. Ten building design variables are selected, which could generate approximately 300,000 solutions. The developed methodology reduced the numbers to 14,400 solutions and found seven Pareto solutions that comply with the three objectives and their constraints. High energy-efficient building envelope, appropriate window-wall ratio and window type, natural ventilation during the day, and night cooling can play a key role in achieving the objectives under current weather conditions. An additional cool roof and external overhang will be needed in the medium-term future climate, and an additional movable screen shading will be needed in the long-term future climate.