This article presents an identification benchmark based on data from a public swimming pool in operation. Such a system is both a complex process and easily understandable by all with regard to the stakes. Ultimately, the objective is to reduce the energy bill while maintaining the level of quality of service. This objective is general in scope and is not limited to public swimming pools. This can be done effectively through what is known as economic predictive control. This type of advanced control is based on a process model. It is the aim of this article and the considered benchmark to show that such a dynamic model can be obtained from operating data. For this, operational data is formatted and shared, and model quality indicators are proposed. On this basis, the first identification results illustrate the results obtained by a linear multivariable model on the one hand, and by a neural dynamic model on the other hand. The benchmark calls for other proposals and results from control and data scientists for comparison.

翻译：本文提出了一个基于公共泳池运行数据的辨识基准。此类系统既是一个复杂过程，又因其涉及的关键问题而易于被所有人理解。最终目标是降低能耗，同时维持服务质量水平。该目标具有普遍意义，并不局限于公共泳池。通过所谓的经济预测控制可以有效地实现这一目标。这种先进控制方法依赖于过程模型。本文及所考虑的基准旨在证明，此类动态模型可以从运行数据中获取。为此，对运行数据进行格式化并共享，同时提出了模型质量指标。在此基础上，初步辨识结果一方面展示了线性多变量模型所获得的结果，另一方面展示了神经动态模型所获得的结果。该基准呼吁控制与数据科学领域的研究人员提出其他方案和结果以进行比较。