Multi-Objective Techno-Economic Optimization of Design Parameters for Residential Buildings in Different Climate Zones

Abstract

The comprehensive approach for a building envelope design involves building performance simulations, which are time-consuming and require knowledge of complicated processes. In addition, climate variation makes the selection of these parameters more complex. The paper aims to establish guidelines for determining a single-family household’s unique optimal passive design in various climate zones worldwide. For this purpose, a bi-objective optimization is performed for twenty-four locations in twenty climates by coupling TRNSYS and a non-dominated sorting genetic algorithm (NSGA-III) using the Python program. The optimization process generates Pareto fronts of thermal load and investment cost to identify the optimum design options for the insulation level of the envelope, window aperture for passive cooling, window-to-wall ratio (WWR), shading fraction, radiation-based shading control, and building orientation. The goal is to find a feasible trade-off between thermal energy demand and the cost of thermal insulation. This is achieved using multi-criteria decision making (MCDM) through criteria importance using intercriteria correlation (CRITIC) and the technique for order preference by similarity to ideal solution (TOPSIS). The results demonstrate that an optimal envelope design remarkably improves the thermal load compared to the base case of previous envelope design practices. However, the weather conditions strongly influence the design parameters. The research findings set a benchmark for energy-efficient household envelopes in the investigated climates. The optimal solution sets also provide a criterion for selecting the ranges of envelope design parameters according to the space heating and cooling demands of the climate zone.

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

“…As research and the evolution of algorithms progress, scholars are also experimenting with a variety of algorithms like NSGA-III [37,61], SPEA-II [62], HypE [63], MOPSO, and MOEA/E [41,64]. Usman, M., and others achieved optimal passive design for single-family homes in different climates by coupling the NSGA-III genetic algorithm with the building energy simulation tool TRNSYS [65]. Mostafazadeh used an improved version of the NSGA-III algorithm, prNSGA-III, as the research optimization algorithm [61].…”

Carbon emission reduction in public buildings of extreme cold regions: A study on enclosure structure and HVAC system parameter optimization

“…The Criteria Importance Through Intercrieria Correlation (CRITIC) method provides a systematic way to handle multiobjective optimization to find the optimal solution by accounting for conflicting relationships among objectives and estimating their relative importance 18,19 …”

“…[14][15][16][17] The Criteria Importance Through Intercrieria Correlation (CRITIC) method provides a systematic way to handle multiobjective optimization to find the optimal solution by accounting for conflicting relationships among objectives and estimating their relative importance. 18,19 The heating, ventilation and air-conditioning system is responsible for approximately half of the energy consumed. In 2023, the total energy consumption in the U.S. was significantly influenced by the residential and commercial sectors, contributing about 19.7% and 17.2% respectively, amounting to a combined contribution of 36.9%.…”

Buildings’ Heating and Cooling Load Prediction for Hot Arid Climates: A Novel Intelligent Data-Driven Approach

“…Environmental and climatic conditions do not affect the cooling/heating loads of residential construction; this is indisputable. However, sudden weather changes might cause sustainable models to be disrupted, lowering the reliability factor and enhancing the error in the process [25].…”