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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| Vydáno v: | 2025 International Conference on Artificial Intelligence and Digital Ethics (ICAIDE) s. 541 - 544 |
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IEEE
29.05.2025
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Cao, Shupei Zhao, Shuying |
| Author_xml | – sequence: 1 givenname: Shupei surname: Cao fullname: Cao, Shupei email: 778519311@qq.com organization: Guangdong University of Science and Technology,2022Internet of Things Engineering Undergraduate Class3,Guangdong,China – sequence: 2 givenname: Shuying surname: Zhao fullname: Zhao, Shuying email: zhaoshuying682@163.com organization: Guangdong University of Science and Technology,Guangdong,China |
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| Snippet | With the increasing global energy demand and increasingly serious environmental problems, energy efficiency optimization of low-carbon building groups has... |
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| SubjectTerms | 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 |
| Title | Low-carbon Building Group Energy-saving Scheduling Optimization Method Based on Genetic Programming from the Perspective of New Quality Productive Forces |
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