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Ιδρυματικό Αποθετήριο [SANDBOX]
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Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/A949E50D-C745-4A35-A9FF-ED897E882C1E | - |
| Αναγνωριστικό | https://doi.org/10.3390/en11123376 | - |
| Αναγνωριστικό | https://www.mdpi.com/1996-1073/11/12/3376 | - |
| Γλώσσα | en | - |
| Μέγεθος | 23 pages | en |
| Τίτλος | Simulation-based evaluation and optimization of control strategies in buildings | en |
| Δημιουργός | Kontes Georgios | en |
| Δημιουργός | Κοντες Γεωργιος | el |
| Δημιουργός | Giannakis Georgios | en |
| Δημιουργός | Γιαννακης Γεωργιος | el |
| Δημιουργός | Sánchez Victor | en |
| Δημιουργός | De Agustin-Camacho Pablo | en |
| Δημιουργός | Romero-Amorrortu Ander | en |
| Δημιουργός | Panagiotidou Natalia | en |
| Δημιουργός | Rovas Dimitrios | en |
| Δημιουργός | Ροβας Δημητριος | el |
| Δημιουργός | Steiger Simone | en |
| Δημιουργός | Mutschler Christopher | en |
| Δημιουργός | Gruen Gunnar | en |
| Εκδότης | Multidisciplinary Digital Publishing Institute | en |
| Περίληψη | 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. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2019-05-24 | - |
| Ημερομηνία Δημοσίευσης | 2018 | - |
| Θεματική Κατηγορία | Data-driven control | en |
| Θεματική Κατηγορία | Energyplus | en |
| Θεματική Κατηγορία | Model predictive control in buildings | en |
| Θεματική Κατηγορία | Multi-criteria decision analysis | en |
| Θεματική Κατηγορία | Reinforcement learning | en |
| Θεματική Κατηγορία | Simulation model | en |
| Θεματική Κατηγορία | Climate control | en |
| Θεματική Κατηγορία | Climate models | en |
| Βιβλιογραφική Αναφορά | G. D. Kontes, G. I. Giannakis, V. Sánchez, P. De Agustin-Camacho, A. Romero-Amorrortu, N. Panagiotidou, D. V. Rovas, S. Steiger, Ch. Mutschler and G. Gruen, "Simulation-based evaluation and optimization of control strategies in buildings," Energies, vol. 11, no. 12, Dec. 2018. doi: 10.3390/en11123376 | en |
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