Comparative study of neural network based and white box model predictive control for a room temperature control application

Journal of Physics Conference Series, vol. 2042, pp. 012043

Abstract

Abstract On a global scale, buildings are a major cause for primary energy consumption. Since buildings are complex multiple input multiple output systems and characterized by slow dynamics, model predictive control is a promising approach to reduce building energy consumption. Due to the high individual modeling effort model predictive control lacks practical applicability. For that reason black box process models are gaining more and more interest in scientific literature. In this work we evaluate the performance of an ANN based controller against a white box controller with perfect knowledge. We show that the data driven controller achieves a similar control quality as the white box controller. We initially train the data driven controller in 20 days and then employ an online learning strategy to continuously improve the control quality.

Authors 5

1. Phillip Stoffel corresponding Aachen E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, 52074 Aachen, Germany

2. Max Berktold Aachen E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, 52074 Aachen, Germany

3. Arman Gall Aachen E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, 52074 Aachen, Germany

4. Alexander Kiimpel Aachen E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, 52074 Aachen, Germany

5. Dirk Müller Aachen E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, 52074 Aachen, Germany