In this paper, we propose a new end-to-end methodology to optimize the energy performance as well as comfort and air quality in large buildings without any renovation work. We introduce a metamodel based on recurrent neural networks and trained to predict the behavior of a general class of buildings using a database sampled from a simulation program. This metamodel is then deployed in different frameworks and its parameters are calibrated using the specific data of two real buildings. Parameters are estimated by comparing the predictions of the metamodel with real data obtained from sensors using the CMA-ES algorithm, a derivative free optimization procedure. Then, energy consumptions are optimized while maintaining a target thermal comfort and air quality, using the NSGA-II multi-objective optimization procedure. The numerical experiments illustrate how this metamodel ensures a significant gain in energy efficiency, up to almost 10%, while being computationally much more appealing than numerical models and flexible enough to be adapted to several types of buildings.
翻译:在本文中,我们提出了一个新的端对端方法,以优化大型建筑的能源性能以及舒适和空气质量,而不进行任何翻新工作。我们引入了一个基于经常性神经网络的元模型,并经过培训,利用模拟程序抽样的数据库预测一般建筑类别的行为。随后,该元模型被部署在不同的框架中,其参数则根据两个真实建筑的具体数据加以校准。参数是通过比较元模型的预测与使用利用衍生自由优化程序CMA-ES算法的传感器获得的真实数据来估算的。然后,在保持目标热舒适和空气质量的同时,利用NSGA-II多目标优化程序优化能源消费。数字实验表明,该元模型如何确保能源效益显著提高,达到近10%,同时计算出比数字模型更具吸引力和灵活性,足以适应几类建筑。