Argiriou, A.A. and Bellas-Velidis, I. and Kummert, M. and Andre, P. (2004) A neural network controller for hydronic heating systems of solar buildings. Neural Networks, 17 (3). pp. 427-440. ISSN 0893-6080Full text not available in this repository. (Request a copy from the Strathclyde author)
An artificial neural network (ANN)-based controller for hydronic heating plants of buildings is presented. The controller has forecasting capabilities: it includes a meteorological module, forecasting the ambient temperature and solar irradiance, an indoor temperature predictor module, a supply temperature predictor module and an optimizing module for the water supply temperature. All ANN modules are based on the Feed Forward Back Propagation (FFBP) model. The operation of the controller has been tested experimentally, on a real-scale office building during real operating conditions. The operation results were compared to those of a conventional controller. The performance was also assessed via numerical simulation. The detailed thermal simulation tool for solar systems and buildings TRNSYS was used. Both experimental and numerical results showed that the expected percentage of energy savings with respect to a conventional controller is of about 15% under North European weather conditions.
|Keywords:||Artificial Neural Networks, Heating System Control, Hydronic Systems, Neural Controller, Energy Savings, Thermal Simulation, Mechanical engineering and machinery, Artificial Intelligence, Cognitive Neuroscience|
|Subjects:||Technology > Mechanical engineering and machinery|
|Department:||Faculty of Engineering > Mechanical and Aerospace Engineering|
|Depositing user:||Strathprints Administrator|
|Date Deposited:||07 Jan 2008|
|Last modified:||22 Mar 2017 09:39|