A neural network controller for hydronic heating systems of solar buildings

Authors:
Athanassios A. Argiriou;Ioannis Bellas-Velidis;Michaël Kummert;Philippe André
Affiliations:
Section of Applied Physics, Department of Physics, University of Patras, GR-256 00 Patras, Greece;Institute for Astronomy and Astrophysics, National Observatory of Athens, I. Metaxa & V. Pavlou, GR-152 36 Palaia Pendeli, Greece;Solar Energy Laboratory, University of Wisconsin-Madison, 1500 Engineering Drive, 1303 ERB, Madison, WI and Fondation Universitaire Luxembourgeoise, 165 Avenue de Longwy, B-6700 Arlon, Belgium;Fondation Universitaire Luxembourgeoise, 165 Avenue de Longwy, B-6700 Arlon, Belgium
Venue:
Neural Networks
Year:
2004

Citing 2
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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.