Mostra el registre d'ítem simple
Implementation of predictive control in a commercial building energy management system using neural networks
dc.contributor.author | Macarulla Martí, Marcel |
dc.contributor.author | Casals Casanova, Miquel |
dc.contributor.author | Forcada Matheu, Núria |
dc.contributor.author | Gangolells Solanellas, Marta |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Projectes i de la Construcció |
dc.date.accessioned | 2017-07-27T12:00:42Z |
dc.date.available | 2019-07-01T08:06:01Z |
dc.date.issued | 2017-09-15 |
dc.identifier.citation | Macarulla, M., Casals, M., Forcada, N., Gangolells, M. Implementation of predictive control in a commercial building energy management system using neural networks. "Energy and buildings", 15 Setembre 2017, vol. 151, p. 511-519. |
dc.identifier.issn | 0378-7788 |
dc.identifier.uri | http://hdl.handle.net/2117/106961 |
dc.description.abstract | Most existing commercial building energy management systems (BEMS) are reactive rule-based. This means that an action is produced when an event occurs. In consequence, these systems cannot predict future scenarios and anticipate events to optimize building operation. This paper presents the procedure of implementing a predictive control strategy in a commercial BEMS for boilers in buildings, and describes the results achieved. The proposed control is based on a neural network that turns on the boiler each day at the optimum time, according to the surrounding environment, to achieve thermal comfort levels at the beginning of the working day. The control strategy presented in this paper is compared with the current control strategy implemented in BEMS that is based on scheduled on/off control. The control strategy was tested during one heating season and a set of key performance indicators were used to assess the benefits of the proposed control strategy. The results showed that the implementation of predictive control in a BEMS for building boilers can reduce the energy required to heat the building by around 20% without compromising the user’s comfort. |
dc.format.extent | 9 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Edificació |
dc.subject.lcsh | Building--Energy conservation |
dc.subject.lcsh | Predictive control |
dc.subject.lcsh | Boilers--Efficiency |
dc.subject.other | Building energy management system |
dc.subject.other | Energy savings |
dc.subject.other | Boiler management |
dc.subject.other | Neural networks |
dc.title | Implementation of predictive control in a commercial building energy management system using neural networks |
dc.type | Article |
dc.subject.lemac | Control predictiu |
dc.subject.lemac | Edificis -- Estalvi d'energia |
dc.subject.lemac | Calderes -- Control autòmatic |
dc.subject.lemac | Calderes -- Eficiència |
dc.contributor.group | Universitat Politècnica de Catalunya. GRIC - Grup de Recerca i Innovació de la Construcció |
dc.identifier.doi | 10.1016/j.enbuild.2017.06.027 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://www.sciencedirect.com/science/article/pii/S0378778817300907 |
dc.rights.access | Open Access |
local.identifier.drac | 21164108 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Macarulla, M.; Casals, M.; Forcada, N.; Gangolells, M. |
local.citation.publicationName | Energy and buildings |
local.citation.volume | 151 |
local.citation.startingPage | 511 |
local.citation.endingPage | 519 |
Fitxers d'aquest items
Aquest ítem apareix a les col·leccions següents
-
Articles de revista [360]
-
Articles de revista [168]