Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Local vs. integrated control of a variable refrigerant flow system using artificial neural networks
Existing studies have treated variable refrigerant flow (VRF) control as a local control problem where control variables are determined using only local state information. This study investigates an integrated VRF control in which the VRF control actions are determined based on not only local information but also the dynamics of the room it serves. For this purpose, two artificial neural network simulation models were developed: one to predict indoor air temperature of the room and the other to predict the VRF’s compressor power. The ANN simulation models were validated with 192 experiments conducted in an experimental chamber. The results revealed that the integrated control reduced cooling and compressor energy use of the VRF by 21.6% and 13.1%, respectively, compared to the local control. These energy savings were achieved because the integrated control ANN models were aware of the dynamic relationship between the VRF and the target room.
Local vs. integrated control of a variable refrigerant flow system using artificial neural networks
Existing studies have treated variable refrigerant flow (VRF) control as a local control problem where control variables are determined using only local state information. This study investigates an integrated VRF control in which the VRF control actions are determined based on not only local information but also the dynamics of the room it serves. For this purpose, two artificial neural network simulation models were developed: one to predict indoor air temperature of the room and the other to predict the VRF’s compressor power. The ANN simulation models were validated with 192 experiments conducted in an experimental chamber. The results revealed that the integrated control reduced cooling and compressor energy use of the VRF by 21.6% and 13.1%, respectively, compared to the local control. These energy savings were achieved because the integrated control ANN models were aware of the dynamic relationship between the VRF and the target room.
Local vs. integrated control of a variable refrigerant flow system using artificial neural networks
Ahn, Kiuhn (Autor:in) / Kim, Kyung Jae (Autor:in) / Song, Kwanwoo (Autor:in) / Park, Cheol soo (Autor:in)
Science and Technology for the Built Environment ; 26 ; 1117-1131
13.09.2020
15 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Hybrid model using Bayesian neural network for variable refrigerant flow system
| Taylor & Francis Verlag | 2022
Integrated Emitter Local Loss Prediction Using Artificial Neural Networks
| British Library Online Contents | 2010
Editorial: Variable refrigerant flow technology
| Taylor & Francis Verlag | 2015
Multi-variable extremum seeking control for a multi-functional variable refrigerant flow system
| Taylor & Francis Verlag | 2018