A holistic passive design approach to optimize indoor environmental quality of a typical residential building in Hong Kong

Highlights

• A holistic optimization approach for a passively designed building is presented.

• NSGA-II algorithm is coupled with EnergyPlus to conduct the modelling experiment.

• Sensitivity analyses are conducted to screen out significant influential factors.

• Different ranking methods are applied to further process Pareto optimal solutions.

• Synergy of energy and indoor environment is considered in reaching final solutions.

Abstract

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.

Introduction

The process of green building design requires the project team to constantly hold in mind the building performance in terms of the energy efficiency, material use, indoor environment quality (IEQ) and so forth. To achieve a sustainable goal in building projects, designers and engineers can consider exploiting passive design features such as the building layout, envelop thermophysics, building geometry and infiltration & air-tightness, which have been proved to significantly affect the building performance in many studies [1], [2]. IEQ is considered an important aspect of the building assessment, as most urban residents spend 80%–90% of their time indoor [3]. Green building rating schemes such as BEAM Plus in Hong Kong has been highlighting the importance of IEQ by laying down multiple criteria including the lighting quality, indoor air quality, acoustics, ventilation and thermal comfort conditions [4]. Some key aspects of IEQ can be greatly influenced by different passive architectural designs, which should be investigated by a holistic optimization process based on in-depth and exhaustive sensitivity analyses (SA). It is essential for designers to understand the relative importance of each strategy and deploy them appropriately at the first opportunity.

Multiples building design factors can be subject to extensive and systematic examinations according to different SA approaches with the assistance of building simulation tools. The local sensitivity analysis is used to examine the energy performance of office buildings in Hong Kong with DOE-2 [5]. Instead of the whole building sensitivity analysis, the building envelope was solely investigated to decide the optimum slab thickness for floors, ceiling and external walls by considering the variation of indoor operative temperature caused by the thermal mass [6]. The window aperture area was also independently correlated with the peak electricity demand and annual energy consumption to provide simple design charts for engineers in early planning stages [7]. The global sensitivity analysis is adopted to study the uncertainty and sensitivity of a passively cooled office building in a moderate climate [8]. According to the findings, the indoor thermal comfort condition, evaluated by the weighted temperature excess hours (WTE), was mostly perturbed by the single-sided ventilation. Yildiz and Arsan estimated the impact of design parameters of low-rise apartment buildings in hot and humid climates with the Monte Carlo method [9]. Heating and cooling loads for different floors were selected as SA outputs, on which the total window area, heat transfer coefficient and solar heat gain coefficient were proved to have the greatest impact. High-rise residential buildings were also assessed by the deviation of energy demand from the baseline scenarios in five Chinese climate zones [10]. In addition, a couple of SA studies also validated the potential thermal load reduction by adjusting the building shape factor, envelope thermal resistance or occupant behavior [11], [12], [13].

Based on the screened significant design factors form sensitivity analyses, optimization studies can be further conducted to facilitate the building design process and improve the project cost effectiveness, energy efficiency or indoor environment comfort. Carlucci et al. carried out a four-objective optimization study on a detached zero-carbon house in Italy and acquired the Pareto frontier of the variants to minimize thermal and visual discomfort [14]. Futrell et al. tried to minimize the cooling, heating and lighting energy demand using GenOpt with Hooke Jeeves and Particle Swarm Optimization algorithms [15]. In a similar work, a software platform developed by QT language and OpenGL interface is used to perform energy saving optimization with the Multi-island Genetic Algorithm (GA) [16]. Design optimization problems including the window feature, building orientation and wall reflectance were thoroughly investigated with extensive daylight indices as objectives [17]. Optimal solutions were screened out according to their appearance frequency in 6 sets of Pareto frontiers and their mean distances to utopia points. Multi-objective optimization as a holistic building design approach in early stages was also explored in similar studies with criteria such as the daylight, energy use, thermal comfort and capital cost [18], [19]. Besides GA methods, a multi-objective particle swarm optimization (MOPSO) algorithm was exploited to search for a set of non-dominated solutions for a single room model in four major climatic regions of Iran [20]. Ruiz et al. proposed a methodology to accurately perform automated envelope calibration under the International Performance Measurement and Verification Protocol (IPMVP). A highly reliable building energy simulation model for detailed analysis of energy saving strategies was obtained by the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Furthermore, Ortiz et al. conducted a cost-optimal study for refurbishment of residential buildings in Spain by simultaneously considering the thermal comfort, energy use and economic criteria [21].

According to the above introduction and brief review of state-of-art, it can be recognized that there is little research in combined sensitivity and optimization analyses of all related indoor environmental assessment criteria for a passively design high-rise residential building. This paper mainly focuses on a multi-objective (including daylight, ventilation and thermal comfort) optimization of a generic building model with selected significant input design variables based on a comprehensive sensitivity analysis which thoroughly explores the whole feasible range of building thermal and lighting properties. NSGA-II coupled with jEPlus and EnergyPlus models was adopted to obtain the Pareto frontier based on IEQ assessment objectives and constraints from the BEAM Plus labelling system in Hong Kong. Consequently, a single final solution is derived from different decision making methods which carefully consider the synergy of energy and indoor environment performance.