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The green building assessment emphasizes the indoor environment quality (IEQ) by looking into the indoor air quality, lighting quality, acoustics, ventilation and thermal comfort conditions, which can be enhanced by effective initiatives at the early design stage. Designers and engineers usually consider exploiting passive designs to achieve a sustainable goal in building projects. In such background, this paper presents a holistic passive design approach by incorporating a robust sensitivity analysis to an efficient multi-objective optimization process to assess a typical high-rise residential building in hot and humid regions like Hong Kong. EnergyPlus and jEPlus are adopted to conduct modelling experiments with an input parametric matrix generated by the Latin Hypercube Sampling (LHS). All related indoor environment performance indices including the daylight, natural ventilation and thermal comfort are treated as optimization objectives and constraints to fulfil the local green building guidance. The non-dominated sorting genetic algorithm (NSGA-II) is coupled with jEPlus to obtain the Pareto frontier by thoroughly searching the problem space constructed with screened out significant input variables from the sensitivity analysis. Furthermore, different post-optimization analysis methods are applied to decide the final optimum solution, where the total unmet time decreased by 11.2% in contrast with the baseline case. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive SA (sensitivity analysis) of the typical PRH (public rental housing) development in Hong Kong based on a combined building energy, daylight and AFN (airflow network) simulation. A generic building model is constructed with a proposed MM (mixed-mode) ventilation control strategy to fulfill the thermal comfort requirement in the local green building guidance. The numerical modeling results are used to conduct both local and global sensitivity analyses to determine the relative importance of major passive design parameters, which comprehensively cover design aspects of the building layout, envelop thermophysics, building geometry and infiltration & air-tightness. The calculated global and local sensitivity indices on the cooling energy prove that the window solar heat gain coefficient, window to ground ratio, external obstruction and overhang projection fraction are the four most influential passive design factors. Similar results are also obtained when the lighting energy is specified as the output of the sensitivity analysis. The optimized building model derived from the sensitivity analysis is anticipated to achieve an energy saving of 41.6% compared to the baseline model as stipulated by the local building regulation. It is believed that sensitivity analysis is useful for identifying crucial design parameters to facilitate further optimization of the building performance in early architectural design stages. [ABSTRACT FROM AUTHOR]

Solar energy is the most popular resource for power generation among the various available renewable energy alternatives. Solar radiation data are important for solar systems and energy-efficient building designs. Due to the unavailability of measurement, solar radiation prediction models are required. Recently, machine learning techniques were successfully used for predicting solar radiation. However, previous works were mainly focusing on monthly average daily or daily solar radiation. In this study, models for predicting hourly global solar radiation on a horizontal surface were developed based on Multivariate Adaptive Regression Spline (MARS) method. Hourly meteorological data measured in 7 years were used for the study. Sensitivity analysis was conducted using MARS algorithm and the most important variables were selected as inputs of the proposed models. 16 MARS models with different combinations of input variables were proposed. Logistic regression and Artificial Neural Networks (ANN) methods were also used to develop models for comparative study. Finally, the proposed models were evaluated against measurements and compared with existing models. The results showed that the proposed MARS models have good performance in both prediction accuracy and interpretability. The proposed models could be used to estimate effectively the hourly solar radiation according to different combinations of measured variables. • Sensitivity analysis was conducted using MARS, five most important climate parameters were selected. • 16 MARS models were proposed using various combinations of most important variables. • Models using logistic regression and ANN methods were built for comparative study. • The proposed MARS models have good performance in both prediction accuracy and interpretability. [ABSTRACT FROM AUTHOR]