Operational optimization of multi-energy systems requires a mathematical model that is accurate and computationally efficient. A model can be generated in a data-driven way if measured data is available. Commonly, data is then used to model each component of the multi-energy system independently. However, independent modeling of each component may lead to models that are unnecessarily complicated and, thus, inefficient in practice.

In this work, we propose the method AutoMoG for Automated data-driven Model Generation of multi-energy systems using piecewise-linear regression. AutoMoG provides Mixed-Integer Linear Programming models of multi-energy systems. To accurately model the overall multi-energy system, AutoMoG balances the errors caused by each component. Model accuracy is measured in terms of operating cost.

In a case study, AutoMoG provides a multi-energy system model with less linear sections than single-component regression Still, AutoMoG retains high accuracy. Thereby, AutoMoG enables efficient data-driven modeling as the basis for multi-energy system optimization.

多能源系统的运行优化需要一个准确且计算效率高的数学模型。如果可获得测量数据，则可以以数据驱动的方式生成模型。通常，然后使用数据独立地对多能源系统的每个组件进行建模。但是，每个组件的独立建模可能会导致模型不必要地复杂化，因此在实践中效率低下。

在这项工作中，我们提出的方法AutoMoG自动配对数据驱动沫德尔ģ使用分段线性回归多能量系统的eneration。AutoMoG提供了多能量系统的混合整数线性规划模型。为了准确地对整个多能源系统进行建模，AutoMoG平衡了每个组件造成的误差。模型准确性是根据运营成本来衡量的。

在一个案例研究中，AutoMoG提供了一种多能量系统模型，其线性截面比单组分回归法还少。因此，AutoMoG可以进行有效的数据驱动建模，以此作为多能源系统优化的基础。