2020/11/26 by Felix Bünning, Bünning, Felix, Adrian Schalbetter +9 · 9 citations
Computer Science · Engineering · Physics and Astronomy · #Advanced Control Systems Optimization #Building Energy and Comfort Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Systems and Control (eess.SY) #cs.LG #cs.SY #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2011.13227
11 pages, 7 figures, 1 table
arxiv created 2020/11/26 · openalex publication_date 2020/11/26 · arxiv updated 2020/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Model Predictive Control in buildings can significantly reduce their energy consumption. The cost and effort necessary for creating and maintaining first principle models for buildings make data-driven modelling an attractive alternative in this domain. In MPC the models form the basis for an optimization problem whose solution provides the control signals to be applied to the system. The fact that this optimization problem has to be solved repeatedly in real-time implies restrictions on the learning architectures that can be used. Here, we adapt Input Convex Neural Networks that are generally only convex for one-step predictions, for use in building MPC. We introduce additional constraints to their structure and weights to achieve a convex input-output relationship for multistep ahead predictions. We assess the consequences of the additional constraints for the model accuracy and test the models in a real-life MPC experiment in an apartment in Switzerland. In two five-day cooling experiments, MPC with Input Convex Neural Networks is able to keep room temperatures within comfort constraints while minimizing cooling energy consumption.