Low-carbon Building Group Energy-saving Scheduling Optimization Method Based on Genetic Programming from the Perspective of New Quality Productive Forces

With the increasing global energy demand and increasingly serious environmental problems, energy efficiency optimization of low-carbon building groups has become an important research direction in the field of modern construction. How to effectively schedule energy consumption in building groups and...

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Bibliographic Details
Published in:2025 International Conference on Artificial Intelligence and Digital Ethics (ICAIDE) pp. 541 - 544
Main Authors: Cao, Shupei, Zhao, Shuying
Format: Conference Proceeding
Language:English
Published: IEEE 29.05.2025
Subjects:
Buildings
Carbon dioxide
carbon emission optimization
Energy consumption
Energy efficiency
energy management
energy-saving scheduling
Genetic algorithms
Genetic programming
Genetic programming algorithm
low-carbon building
Optimization methods
Particle swarm optimization
Schedules
Scheduling
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Summary:With the increasing global energy demand and increasingly serious environmental problems, energy efficiency optimization of low-carbon building groups has become an important research direction in the field of modern construction. How to effectively schedule energy consumption in building groups and reduce carbon emissions has become a core issue that needs to be solved urgently. This study proposes an energy-saving scheduling optimization method for low-carbon building groups based on a genetic programming algorithm, aiming to achieve efficient energy utilization and minimize carbon emissions through an intelligent scheduling scheme. This paper uses a genetic programming algorithm to optimize the energy scheduling strategy in combination with the energy consumption characteristics and climatic conditions of the building groups. By establishing a comprehensive fitness function that maximizes energy efficiency and minimizes carbon emissions, combined with the data of actual building groups, experimental parameters including population size, crossover rate, mutation rate, etc. are designed, and compared with traditional genetic algorithms and particle swarm optimization algorithms.
DOI:10.1109/ICAIDE65466.2025.11189758