Simulation-Based Evaluation and Optimization of Control Strategies in Buildings

Georgios D. Kontes, Georgios I. Giannakis, Víctor Sánchez, Pablo De Agustin-Camacho, Ander Romero-Amorrortu, Natalia Panagiotidou, Dimitrios V. Rovas, Simone Steiger, Christopher Mutschler and Gunnar Gruen
Additional contact information
Georgios D. Kontes: Machine Learning & Information Fusion Group, Precise Positioning and Analytics Department, Fraunhofer Institute for Integrated Circuits IIS, Nordostpark 84, 90411 Nuremberg, Germany
Georgios I. Giannakis: School of Production Engineering and Management, Technical University of Crete, Chania 73100, Greece
Víctor Sánchez: Tecnalia Research & Innovation, Sustainable Construction Division, Parque Tecnológico de Bizkaia, Edificio 700, 48160 Derio, Spain
Pablo De Agustin-Camacho: Tecnalia Research & Innovation, Sustainable Construction Division, Parque Tecnológico de Bizkaia, Edificio 700, 48160 Derio, Spain
Ander Romero-Amorrortu: Tecnalia Research & Innovation, Sustainable Construction Division, Parque Tecnológico de Bizkaia, Edificio 700, 48160 Derio, Spain
Natalia Panagiotidou: Department of Mechanical Engineering and Building Services Engineering, Technische Hochschule Nürnberg Georg Simon Ohm, 90489 Nuremberg, Germany
Dimitrios V. Rovas: The Bartlett School of Environment, Energy and Resources, Faculty of the Built Environment, University College London, London WC1E 6BT, UK
Simone Steiger: Technical Building Systems Group, Nuremberg Branch, Department of Energy Efficiency and Indoor Climate, Fraunhofer Institute for Building Physics, 90429 Nuremberg, Germany
Christopher Mutschler: Machine Learning & Information Fusion Group, Precise Positioning and Analytics Department, Fraunhofer Institute for Integrated Circuits IIS, Nordostpark 84, 90411 Nuremberg, Germany
Gunnar Gruen: Department of Mechanical Engineering and Building Services Engineering, Technische Hochschule Nürnberg Georg Simon Ohm, 90489 Nuremberg, Germany

Energies, 2018, vol. 11, issue 12, 1-23

Abstract: Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings.

Keywords: model predictive control in buildings; reinforcement learning; data-driven control; simulation model; multi-criteria decision analysis; energyplus (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/12/3376/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/12/3376/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:12:p:3376-:d:187229

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().