论文标题
端到端深层建模以校准和优化能量负载
End-to-end deep metamodeling to calibrate and optimize energy loads
论文作者
论文摘要
在本文中，我们提出了一种新的端到端方法，以优化大型建筑物的能源性能以及舒适性，空气质量和卫生。使用模拟程序采样的数据集介绍和训练了基于变压器网络的元模型。然后，使用CMA-ES优化算法和从传感器获得的实际数据校准了一些物理参数和该元模型的构建管理系统设置。最后，使用多目标优化程序获得了最大程度地减少能量载荷的最佳设置，同时保持目标热舒适度和空气质量。数值实验说明了该元模型如何确保能源效率的显着提高，同时计算上比需要大量物理参数的模型更具吸引力。
In this paper, we propose a new end-to-end methodology to optimize the energy performance and the comfort, air quality and hygiene of large buildings. A metamodel based on a Transformer network is introduced and trained using a dataset sampled with a simulation program. Then, a few physical parameters and the building management system settings of this metamodel are calibrated using the CMA-ES optimization algorithm and real data obtained from sensors. Finally, the optimal settings to minimize the energy loads while maintaining a target thermal comfort and air quality are obtained using a multi-objective optimization procedure. The numerical experiments illustrate how this metamodel ensures a significant gain in energy efficiency while being computationally much more appealing than models requiring a huge number of physical parameters to be estimated.
