Automated data-driven modeling of building energy systems via machine learning algorithms

System modeling is a vital part of building energy optimization and control. Grey and white box modeling requires knowledge about the system and a lot of human assistance, which results in costs. In the common case, that information about the system is lacking, the feasibility of grey and white box models decreases further. The installation of sensors and the availability of monitoring data is growing rapidly within building energy systems. This enables the exploitation of statistical modeling, which is already well established in other sectors like computer science and finance. Thus, the present work investigates data-driven machine learning models to explore their potential for modeling building energy systems. The focus is to develop an efficient methodology for data-driven modeling. For this purpose, a comprehensive literature review for detecting optimization methods is conducted. Furthermore, the methodology is implemented in Python and an automated modeling tool is designed. It is used to model various energy systems based on monitoring data; seven use cases on three different systems reveal good results. The models can be used for forecasting, potential analysis, the implementation of various control strategies or as a replacement for missing information within the field of grey box modeling. (c) 2019 Elsevier B.V. All rights reserved.