Multi-Objective Optimization Technology for Building Energy-Saving Renovation Strategy Based on Genetic Algorithm

Abstract

Building energy-saving design is significant for the industry to achieve carbon reduction and sustainable development. Firstly, a multi-objective model for energy consumption, cost, and carbon emissions is established based on the three-dimensional perspectives of society, nature, and economy. Then, a polynomial operator is used to improve the non dominated sorting genetic algorithm to calculate the optimal solution set. The low computational efficiency caused by direct coupling of algorithms in traditional optimization processes is expected to be addressed. According to the results, for the Square1 dataset and Iris dataset, the algorithm proposed in this study improved the reverse distance and convergence metrics by more than 70% compared to support vector machine-genetic algorithm and multi-objective clustering algorithm, with values closer to 0. The solution solved by this algorithm had lower building costs, energy consumption, and carbon emissions, with values of 345200 yuan, 2374 KWh/year, and 26 tons, respectively. This validates the effectiveness of the multi-objective model and solving algorithm established in the study, which helps to obtain the optimal energy-saving design scheme and provides reference for low-carbon optimization of building.