Solar combisystem characterization with a global approach test and a neural network based model identification peer-review under responsibility of PSE AG peer-review under responsibility of PSE AG

Antoine Leconte (1, 2) , Gilbert Achard (2) , Philippe Papillon (1)

1 DTS - Département des Technologies Solaires
2 LOCIE - LabOratoire proCédés énergIe bâtimEnt

The market for small Solar CombiSystems (SCSs) is penalized by its lack of common performance test method. The methodology proposed in this paper is based on the SCSPT procedure which tests each combisystem as a whole system on a semi-virtual test bench. Test results are used to identify a "Gray Box" model of the tested SCS, which includes an Artificial Neural Network. This model can then simulate the behavior of the system for any climate and any building. Results of all those simulations are finally used according to the FSC procedure in order to characterize the tested SCS performances with a simple curve. An experimental study of this methodology with two real SCS is presented in this paper.

Mots clés: Neural networks; solar combisystems; semi-virtual test bench; performance prediction; characterization

Domaines: Sciences de l'ingénieur [physics]

Antoine Leconte, Gilbert Achard, Philippe Papillon. Solar combisystem characterization with a global approach test and a neural network based model identification peer-review under responsibility of PSE AG peer-review under responsibility of PSE AG. Energy Procedia, 2012, 30, pp.1322 - 1330. ⟨10.1016/j.egypro.2012.11.145⟩. ⟨cea-02948535⟩