title: A neural network controller for hydronic heating systems of solar buildings.
abstract: 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.
authors.affiliation: Section of Applied Physics, Department of Physics, University of Patras, GR-256 00 Patras, Greece. argiriou@physics.upatras.gr
authors.forename: Athanassios A
authors.initials: AA
authors.lastname: Argiriou
authors.forename: Ioannis
authors.initials: I
authors.lastname: Bellas-Velidis
authors.forename: Michaël
authors.initials: M
authors.lastname: Kummert
authors.forename: Philippe
authors.initials: P
authors.lastname: André
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D004867:Equipment Design; D005246:Feedback / Q000502:physiology; D006361:Heating / Q000295:instrumentation*; D006801:Humans; D016571:Neural Networks, Computer; D012985:Software Design; D012993:Solar Energy; D013696:Temperature