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：本文提出了一种基于公共游泳池的运行数据的识别基准。这样的系统既是一个复杂的过程，又很容易被所有人理解。最终目标是降低能源账单，同时保持服务质量水平。这个目标是普遍的，并不仅限于公共游泳池。这可以通过所谓的经济预测控制有效地实现。这种高级控制是基于过程模型的。本文考虑的基准旨在表明，这样的动态模型可以从运行数据中获得。为此，操作数据被格式化和共享，并且提出了模型质量指标。在此基础上，第一批识别结果分别通过线性多变量模型和神经动态模型来呈现。该基准呼吁其他控制和数据科学家的提案和结果以便进行比较。