一种基于混合深度学习和克隆选择算法的模型,用于商业建筑的能源消耗预测.
1Moscow Institute of Aeronautics and Technology, Anyang Institute of Technology, Anyang, China.
Science progress
|September 28, 2024
概括
本研究介绍了一种混合深度学习模型,用于商业建筑的能源消耗预测. 新的CNN-GRU-CSA网络 (CGC-Net) 显著提高了预测准确性和效率,有助于可持续能源管理.
科学领域:
- 能源管理 能源管理
- 人工智能的人工智能
- 可持续发展 可持续发展 可持续发展
背景情况:
- 商业建筑是主要的能源消费者,给环境带来了挑战.
- 传统的能源管理方法缺乏准确性和适用性.
- 准确的能源消耗预测对于可持续发展至关重要.
研究的目的:
- 为商业建筑能源消耗预测和节能策略提出混合深度学习模型.
- 提高能源消耗预测的准确性和效率.
- 为商业建筑能源管理提供技术支持.
主要方法:
- 融合卷积神经网络 (CNN),门式循环单元 (GRU) 和克隆选择算法 (CSA).
- 在CNN-GRU-CSA网络 (CGC-Net) 模型的开发.
- 在多个基准数据集上的验证:BDGP,CBECS,NEPB和BEBDEE.
主要成果:
- 在数据集 (15.94-17.12) 中,CGC-Net 实现了较低的平均绝对误差 (MAE).
- 该模型显著优于传统方法和其他深度学习模型.
- 与现有方法相比,证明了更快的培训和推断时间.
结论:
- CGC-Net模型为商业建筑能源管理提供了稳定且优质的解决方案.
- 混合深度学习方法为能源效率提供了创新的解决方案.
- 这项研究为优化商业建筑的能源消耗提供了必要的技术支持.
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