A neural network controller for hydronic heating systems of solar buildings

A.A. Argiriou, I. Bellas-Velidis, M. Kummert, P. Andre

Research output: Contribution to journalArticlepeer-review

71 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)427-440
Number of pages13
JournalNeural Networks
Issue number3
Publication statusPublished - Apr 2004


  • Artificial Neural Networks
  • Heating System Control
  • Hydronic Systems
  • Neural Controller
  • Energy Savings
  • Thermal Simulation


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