Aquesta és una còpia de la versió author's final draft d'un article publicat a la revista Energy and buildings. http://hdl.handle.net/2117/106961 Article publicat / Published paper: Macarulla, M., Casals, M., Forcada, N., Gangolells, M. (2017). Implementation of predictive control in a commercial building energy management system using neural networks. Energy and buildings, This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  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. Building energy management system; energy savings; boiler management; neural networks 3
In particular, the authors in REF proposed a predictive control strategy based on Neural Networks, for boilers control in buildings, to decide the optimal time to switch-on the plant to guarantee energy savings and thermal comfort.
Implementation of predictive control in a commercial building energy management system using neural networks
Energy and Buildings
Engineering