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Energies 2018, 11(12), 3376; https://doi.org/10.3390/en11123376

Simulation-Based Evaluation and Optimization of Control Strategies in Buildings

1
Machine Learning & Information Fusion Group, Precise Positioning and Analytics Department, Fraunhofer Institute for Integrated Circuits IIS, Nordostpark 84, 90411 Nuremberg, Germany
2
Department of Mechanical Engineering and Building Services Engineering, Technische Hochschule Nürnberg Georg Simon Ohm, 90489 Nuremberg, Germany
3
School of Production Engineering and Management, Technical University of Crete, Chania 73100, Greece
4
Tecnalia Research & Innovation, Sustainable Construction Division, Parque Tecnológico de Bizkaia, Edificio 700, 48160 Derio, Spain
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The Bartlett School of Environment, Energy and Resources, Faculty of the Built Environment, University College London, London WC1E 6BT, UK
6
Technical Building Systems Group, Nuremberg Branch, Department of Energy Efficiency and Indoor Climate, Fraunhofer Institute for Building Physics, 90429 Nuremberg, Germany
7
Machine Learning and Data Analytics Lab, Friedrich-Alexander University Erlangen-Nuremberg, Carl-Thiersch-Strasse 2b, 91052 Erlangen, Germany
8
Department of Energy Efficiency and Indoor Climate, Fraunhofer Institute for Building Physics, Fraunhoferstr. 10, 83626 Valley, Germany
*
Author to whom correspondence should be addressed.
Received: 22 October 2018 / Revised: 15 November 2018 / Accepted: 22 November 2018 / Published: 2 December 2018
(This article belongs to the Special Issue 10 Years Energies - Horizon 2028)
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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. View Full-Text
Keywords: model predictive control in buildings; reinforcement learning; data-driven control; simulation model; multi-criteria decision analysis; energyplus model predictive control in buildings; reinforcement learning; data-driven control; simulation model; multi-criteria decision analysis; energyplus
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Kontes, G.D.; Giannakis, G.I.; Sánchez, V.; de Agustin-Camacho, P.; Romero-Amorrortu, A.; Panagiotidou, N.; Rovas, D.V.; Steiger, S.; Mutschler, C.; Gruen, G. Simulation-Based Evaluation and Optimization of Control Strategies in Buildings. Energies 2018, 11, 3376.

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