Multi-Objective Optimization of Envelope Design of Rural Tourism Buildings in Southeastern Coastal Areas of China Based on NSGA-II Algorithm and Entropy-Based TOPSIS Method

Abstract

The rapid development of rural tourism and higher requirements for the indoor environments of rural tourism buildings (RTBs) have led to rapid growth in the energy consumption of RTBs. The aim of this work was to apply a new method to optimize the indoor thermal environments and energy performances of RTBs and promote scientific passive design strategies for RTBs in southeastern coastal areas of China. First, a field survey was carried out to understand the statuses of buildings and the energy consumption of RTBs. Through a building typology analysis, two types of RTBs (renovated from existing buildings and newly built) were chosen as the dominant types in the villages. Second, a comprehensive parametric study was conducted to examine the impact of energy consumption and the indoor thermal environment using a global sensitivity analysis. The passive design parameters with large sensitivity impacts were selected using the Sobol sampling method and by calculating the comprehensive contribution rates of the parameters. Then, the NSGA-II algorithm was used to simultaneously minimize the two objectives and generate the Pareto front solution sets of the two RTB types. Finally, by applying an entropy-based TOPSIS decision-making method, the optimal schemes (the best energy-saving solution, the best comfort solution, and the best compromise solution) for the two RTB types were further obtained from the feasible Pareto-optimal solutions, and the suggested values for the design parameters are presented. This study proposes a new multi-objective optimization approach combining the NSGA-II algorithm and an entropy-based TOPSIS decision-making method, and the findings are valuable, as they can help designers to improve the designs of rural tourism buildings.

Multi-Objective Optimization for Winter Heating Retrofit in Rural Houses of Cold Regions: A Case Study in the Wusu Area

“…Mostafazadeh and others also utilized the TOPSIS method to choose the final strategy [61]. Wang, M., and colleagues, based on the entropy weight TOPSIS decision theory, considered the performance objectives of CEC, ADH, NSE, and GC comprehensively, seeking the best trade-off solution from the set of Pareto optimal solutions, resulting in the best energy-saving scheme, the most comfortable scheme, and the best trade-off scheme in terms of passive design parameters [36].…”

“…With the development of science and technology, the use of performance simulation and algorithm optimization to achieve multi-objective optimization of building performance has become widely used in architectural design and retrofitting. In the field of architecture, multi-objective optimization can be linked with energy consumption simulation software such as EnergyPlus, Ecotect, and TRNSYS and with software like MATLAB or Python to focus on optimizing energy savings, carbon emissions, and economic performance [36]. Chen and others have developed an optimization framework on the Python platform to simulate the minimum carbon emissions, indoor discomfort hours, and overall costs of buildings [37].…”

Research on the Design Strategy of Double–Skin Facade in Cold and Frigid Regions—Using Xinjiang Public Buildings as an Example

“…TOPSIS, a widely utilized comprehensive evaluation method, effectively captures the distinctions among evaluation solutions by fully leveraging the information from the original data. The Entropy Weight TOPSIS evaluation model finds extensive application in the selection of solutions in multi-criteria decision-making [47][48][49]. The specific calculations are outlined as follows:…”

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“…The Non-dominated Sorting Genetic Algorithm-II (NSGA-II), a variant of GA using non-dominated sorting, is widely used in multi-objective BFO studies [20,38,47,74,78]. Goldberg's non-domination technique ranks solutions and orders them by non-domination levels, thereby preserving diversity in the search space [99].…”