Kontes, GD;
Giannakis, GI;
Sánchez, V;
Agustin-Camacho, P;
Romero-Amorrotu, A;
Panagiotidou, N;
Rovas, D;
... Grün, G; + view all
(2018)
Simulation-Based Evaluation and Optimization of Control Strategies in Buildings.
Energies
, 11
(12)
, Article 3376. 10.3390/en11123376.
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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.
| Type: | Article |
|---|---|
| Title: | Simulation-Based Evaluation and Optimization of Control Strategies in Buildings |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/en11123376 |
| Publisher version: | https://www.mdpi.com/1996-1073/11/12/3376 |
| Language: | English |
| Additional information: | © 2018 by the Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | model predictive control in buildings, reinforcement learning, data-driven control, simulation model, multi-criteria decision analysis, energyplus |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10062861 |
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