A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost
Introduction
Population growth, increased demand for indoor environment and global warming have led to a sharp increase in energy consumption for buildings heating and cooling, which accounts for 20% of global energy consumption [1]. Especially, the energy consumption by the residential building is increasing at approximately 30% annually worldwide [2]. Therefore, developing sustainable buildings has increasingly become a very important task, and Passive House (PH) has emerged as the preferred concept for architects and subject for researchers in most countries. PH are buildings that need 80%–90% less heating energy than conventional buildings to provide comfortable indoor conditions, while the incremental cost of their construction is only 5%–10% [3]. Many countries have introduced PH standards. For example, China has issued “Passive ultra-low energy green building technology guidelines” [4]. However, the standards merely set deterministic results to evaluate the design without pre-directing the design. Therefore, it is almost impossible to use standards to fully exploit large design spaces and to guide decision-makers. In addition, engineers and technicians put more focus on active equipment and facilities but ignore the idea that PH is based on improving passive performance [5]. The passive performance is the base of sustainable development, especially for residential buildings [6].
Current research on PH buildings mainly focuses on performance assessment such as energy consumption and thermal comfort. Many studies have shown the superior performance of PH buildings. Based on the performance simulation, the heating energy demand of the PH building is less than the requirement of the PH standard, and the annual cooling energy demand is also very low [7]. The measured data shows that the total energy consumption of PH buildings is reduced by about 50% compared with traditional buildings [8] and reduce heating energy consumption by about 65% and energy consumption by 35% compared with low-energy buildings. However, there are also studies that expose the negative side of PH buildings in the indoor thermal environment, especially during the transitional season. For example, based on in-use monitored data gathered in 21 months, a study evaluated the thermal comfort of a UK PH dwelling with vulnerable occupants [9]. This research indicated that indoor overheating risk (IOR) can occur if the dwelling is not managed correctly, energy and carbon savings should not be at the expense of thermal comfort. In Europe, IOR in PH buildings has been recorded as more widespread compared to traditional buildings [10,11]. Moreover, in Australia, the high-performance buildings do not directly encourage passive survivability and can even increase IOR compared to traditional buildings [12]. Even some studies have directly pointed out that improving building fabric (increased insulation and airtightness) increases IOR [13]. It should be mentioned that natural ventilation is very effective in reducing energy consumption and improving the indoor thermal environment [14]. Opening the window for ventilation during the night or uncomfortable daytime can maintain indoor thermal comfort [15]. In short, thermal comfort issues should be valued in high-performance buildings such as PH buildings.
Many recent scientific studies performed building performance optimization whether it is single or multi-objective. There are numerous metrics involved in assessing building performance: 1) energy metrics [[16], [17], [18], [19], [20]], including annual heating, cooling, lighting, and total building energy demand; 2) life cycle metrics [16], including life cycle costs, life cycle carbon emissions and life cycle energy consumption; 3) indoor thermal comfort metrics [17,18]. It should be mentioned that it is easy to transfer energy metrics into life cycle metrics with some simple additional information [21]. According to the research objects, these studies mainly focus on new residential buildings and renovated buildings. For example, a study developed a new methodology to optimize building life cycle cost, environmental impacts, and occupant satisfaction in the early design phase [17,22]. Another research proposed a set of optimization methods for energy-renovating buildings that focus on energy consumption, retrofit cost, and thermal discomfort hours [23]. However, the design priorities of PH and conventional buildings are different, in the related research of PH buildings, few studies involve integrated optimization of energy efficiency, thermal comfort, and economic benefit.
Computer-aided optimization is the earliest method used in building performance optimization (BPO). It is automatically optimized by coupling simulation software and optimization algorithms [24]. However, evolutionary algorithms usually still need a large number of cost function evaluations before a satisfying result can be obtained [25]. Moreover, higher time costs of dynamic building performance simulation reduce the effectiveness of BPO and especially its diffusion in professional practice [26]. A survey study based on architects, mechanical engineers, and green construction consultants demonstrated that slower optimization speed is the main reason for hindering the actual application of BPO [19]. More important, some optimization indicators cannot be directly obtained by running simulation software, generally, they need secondary processing. Therefore, traditional computer-aided algorithms are not suitable for multi-objective optimization problems. For this reason, an approximation of the optimization problem is required. Among existing approximation approaches, the functional approximation approach (a.k.a. meta-model or surrogate model approximation) is the most used in BPO [27]. The meta-model constructs a functional relationship between multiple inputs and multiple outputs, which improve efficiency by sacrificing precision [28]. It should be mentioned that the meta-model is not equal to the agent model. The former is generally used for building performance optimization problems, while the latter generally appears in research related to human behavior and perception [29]. Seeking appropriate algorithms to build a high-precision meta-model is getting more and more research attention [[30], [31], [32], [33]]. Several studies have employed a variety of algorithms to establish meta model, such as multiple linear regression [[34], [35], [36]], support vector machines (SVM) [33] and artificial neural networks (ANN) [17,23,37,38]. These meta-models are generally combined with optimization algorithms such as genetic algorithms (GA). For example, Asadi et al. present a multi-objective optimization model using GA and ANN to quantitatively assess technology choices in a building retrofit project [23]. Gou et al. established a model to optimize the thermal comfort and energy demand of new residential buildings, by using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) coupled with the ANN [17]. Although the meta-model has been generally accepted and applied, proposing new algorithms to build a more robust and highly accurate meta-model is still the research focus. Especially for multi-objective optimization problems, the robustness of the meta-model is more important [39].
Based on the above analysis, a summary of the existing problems and the corresponding innovations is as follows:
- (1)
From the optimization model:
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The existing problems on PH buildings: Most research related to PH buildings focuses on performance assessments, some of which only raised the phenomenon of the indoor overheating risk in PH buildings, but did not propose solutions to solve the problem. In addition, PH standards have only constrained the range of energy consumption and certain design parameters, but it is difficult to guarantee an optimal solution. In summary, there is still a lack of systematic optimization methods to guide the passive design of PH buildings in engineering applications.
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The corresponding innovation: This paper establishes an optimization model for PH buildings. Different from previous research, the optimization model considers the effect of windowing for natural ventilation on the indoor thermal environment. The relationship between 20 passive design parameters and two building properties including energy demand and thermal comfort was constructed and the optimization scheme was explored under the constraints of PH standards. Finally, an economic analysis of the Pareto frontier solution was carried out. The optimization framework produces more practical and detailed design guidance.
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- (2)
From the optimization method:
- ●
The existing problems on passive optimization: Existing research generally uses meta-models coupled multi-objective optimization algorithm to improve optimization efficiency. Although the effectiveness of the meta-model has been proven in numerous architectural performance optimization studies, seek alternative algorithms suitable for all building performance to build robust and highly accurate meta-models is still the focus of current research.
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The corresponding innovation: This paper proposes a three-stage optimization method (RDA-GBDT-NSGA) that simultaneously improves the optimization efficiency and accuracy. The GBDT machine learning algorithm (described in Section 2.3.2) is used to establish meta-models of building performance, which was first applied to the building optimization. From the two aspects of accuracy and robustness, its effectiveness is verified by comparison with the commonly used algorithms including ANN and SVM.
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Section snippets
Method
The optimization framework for PH buildings mainly includes three parts: constructing optimization model, establishing optimization method and post-Pareto analysis (Fig. 1). This section details the involved methods.
Setting the building model
Since the optimization of passive parameters exists in the early stage of building design, as in most of the related existing studies, the simulation data is used to expand the research database [16]. The entire building performance is simulated using EnergyPlus (Ver.9.0.1), which is a highly validated simulation engine widely used in building energy analysis [70]. In order to represent the characteristics of most Chinese residential buildings, this paper takes the popular slab-type building as
Multivariate performance analysis
PRCC, the sensitivity index, derived from MLR is applied to analyze the main factors affecting building performance. The larger the sample size, the more stable the indicators will be. However, there is no a priori exact rule for determining the adequate sample size for LHS–PRCC index. A way to solve the problem is to systematically increase the sample size and check if the sensitivity index used can consistently capture and rank a similar set of most important effects [75]. Therefore, the
Conclusion
This paper proposes a multi-objective optimization method for PH design, and the cold climate zone is chosen as an example to construct the multi-objective optimization model. Annual energy demand and comfort level are used as optimization objectives, and the boundaries of design parameters, annual cooling, and annual heating energy demand are used as constraints. The proposed three-stage optimization design method can reduce the complexity of the model and improve optimization accuracy and
Declaration of competing interest
The authors declared that they have no conflicts of interest to this work.
We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
Acknowledgment
This research has been supported by the “National Key R&D Program of China” (Grant No. 2016YFC0700100).
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