Data-Driven Multi-Objective Genetic Algorithm for Energy Simulation and Optimization

Abstract

In response to the bottleneck phase in China's foundational construction projects, this paper explores innovative urban development strategies with a focus on the renovation of old residential areas. The study highlights the importance of analyzing building energy consumption as a means to support sustainable development through advanced scientific calculations and simulation optimization techniques. By introducing a data-driven approach and employing a multi-objective genetic algorithm for energy simulation, the research provides a robust framework for optimizing energy use in renovated buildings. This framework is validated using real-world operational data and offers practical solutions to balance energy efficiency, cost-effectiveness, and environmental sustainability in the context of building renovations. The findings contribute to achieving China's “dual carbon” targets and promote a transition towards more sustainable urban development practices.