An Automated Machine Learning Approach towards Energy Saving Estimates in Public Buildings

Reducing the energy consumption of buildings in the public sector is an important component in our efforts towards reaching our sustainability goals. In this context, a decisive prerequisite for administrations and policy makers is a tool for estimating the effectiveness of measures to reduce energy consumption. Estimating the impact of planned investments in building technology at scale, however, remains challenging, mainly for two reasons. For one, accurate physical modeling requires detailed building data, which can be difficult to obtain. Second, adapting established building models to novel measures aiming at energy consumption reduction is difficult. Hence, modeling building consumption patterns after retrofitting is a non-trivial task, and more research is needed to improve modeling techniques as well as to assess their effectiveness across a wide range of application scenarios. Modeling tools need to be generic enough to enable modeling of a variety of building types, they should ideally require as few input features as possible and they should allow for a high degree of automation in the selection and calibration of building modeling tools. Here, we propose a novel machine learning approach that does not require detailed building data and can automatically adapt to retrofitting measures. We evaluate our method on a data set of 113 public buildings in 4 building categories in Berlin, Germany. The data set contains energy consumption data in the initial state and after implementation of a weather-predictive heating control system. Despite being fully automated and requiring only minimal information about the building, our model can reliably predict the energy consumption of large public buildings better than established methods. All code and data are publicly released.