| Título |
Selective distributed model predictive control for comfort satisfaction in multi-zone buildings |
| Autores |
Roshany-Yamchi S. , Witheepanich K. , ESCAÑO GONZÁLEZ, JUAN MANUEL, McGibney A. , Rea S. , Kloetzer M. , Ferariu L. |
| Publicación externa |
No |
| Medio |
2017 21st International Conference On System Theory, Control And Computing (icstcc) |
| Alcance |
Capítulo de un Libro |
| Naturaleza |
Científica |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040591112&doi=10.1109%2fICSTCC.2017.8107109&partnerID=40&md5=8aa8eba0c00996a5bc06ea1a4001a74c |
| Fecha de publicacion |
01/01/2017 |
| Scopus Id |
2-s2.0-85040591112 |
| DOI |
10.1109/ICSTCC.2017.8107109 |
| Abstract |
Distributed model predictive control (DMPC) for thermal regulation in multi-zone buildings continues to gain attention over centralized approaches. Particularly, centralized control approaches have been shown to become impractical when applied to large-scale buildings due to for example, computation complexity, modeling complexity of large buildings and availability of required sensor and actuator infrastructure. In this paper a novel selective DMPC algorithm is developed in which, each agent optimizes a cost function to minimize the control effort in order to save energy and satisfying the comfort bound. The proposed method is useful especially in building thermal regulation when the objective is to keep the temperature of each zone in the building within the defined comfort bound. © 2017 IEEE. |
| Palabras clave |
Buildings; Constraint theory; Cost functions; Energy management; System theory; Thermal variables control; Building energy managements; Centralized approaches; Computation complexity; Distributed Model predictive Control; Energy optimization; Large scale buildings; Sensor and actuators; Soft constraint; Model predictive control |
| Miembros de la Universidad Loyola |
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