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

Author

Listed:
  • Ning Li

    (Shaanxi Nongfa Digital Intelligence Group Co., Ltd., China & Shaanxi Agricultural Development Group Co., Ltd., China)

  • Lei Zhang

    (College of Architecture, Xi'an University of Architecture and Technology, China)

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.

Suggested Citation

  • Ning Li & Lei Zhang, 2025. "Data-Driven Multi-Objective Genetic Algorithm for Energy Simulation and Optimization," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global Scientific Publishing, vol. 16(1), pages 1-23, January.
    • Handle: RePEc:igg:jaeis0:v:16:y:2025:i:1:p:1-23
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