Paper Conference

Proceedings of Building Simulation 2021: 17th Conference of IBPSA


Energy prediction under changed demand conditions: robust machine learning models and input feature combinations

Thomas Schranz 1, Johannes Exenberger 1, Christian Møldrup Legaard 2, Ján Drgona 3, Gerald Schweiger 1
1 Graz University of Technology, Austria
2 Aarhus University, Denmark
3 Pacific Northwest National Laboratory, USA

Abstract: Deciding on a suitable algorithm for energy demand prediction in a building is non-trivial and depends on the availability of data. In this paper we compare four machine learning models, commonly found in the literature, in terms of their generalization performance and in terms of how using different sets of input features affects accuracy. This is tested on a data set where consumption patterns differ significantly between training and evaluation because of the Covid-19 pandemic. We provide a hands-on guide and supply a Python framework for building operators to adapt and use in their applications.
Keywords: Machine Learning, Energy Prediction, Model Robustness, Deep Learning
Pages: 3268 - 3275