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Numerical and experimental results of a novel and generic methodology for energy performance evaluation of thermal systems using renewable energies

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  • Lazrak, Amine
  • Leconte, Antoine
  • Chèze, David
  • Fraisse, Gilles
  • Papillon, Philippe
  • Souyri, Bernard

Abstract

At present there is no reliable approach to model and characterize thermal systems using renewable energy for building applications based on experimental data. The results of the existing approaches are valid only for specific conditions (climate type and thermal building properties). The aim of this paper is to present a generic methodology to evaluate the energy performance of such systems. Artificial neural networks (ANNs) have proved to be suitable to tackle such complex problems, particularly when the system to be modelled is compact and cannot be divided up during the testing stage. Reliable “black box” ANN modelling is able to identify global models of the whole system without any advanced knowledge of its internal operating principles. The knowledge of the system’s global inputs and outputs is sufficient. The proposed methodology is applied to evaluate three different Solar Combisystems (SCSs) combined with a gas boiler or a heat pump (HP) as an auxiliary system. The results show that the best ANN models were able to predict with a satisfactory degree of precision, the annual energy consumption of the all systems except the SCS combined with air source HP, in different conditions, based on a learning sequence lasting only 12days. In fact, the annual energy prediction errors were less than 10% in most cases. The methodology limitations appear in extreme boundary conditions (Barcelona climate) compared to those used during the ANN training process.

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  • Lazrak, Amine & Leconte, Antoine & Chèze, David & Fraisse, Gilles & Papillon, Philippe & Souyri, Bernard, 2015. "Numerical and experimental results of a novel and generic methodology for energy performance evaluation of thermal systems using renewable energies," Applied Energy, Elsevier, vol. 158(C), pages 142-156.
    • Handle: RePEc:eee:appene:v:158:y:2015:i:c:p:142-156
    DOI: 10.1016/j.apenergy.2015.08.049
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    Cited by:

    1. Menegon, Diego & Soppelsa, Anton & Fedrizzi, Roberto, 2017. "Development of a new dynamic test procedure for the laboratory characterization of a whole heating and cooling system," Applied Energy, Elsevier, vol. 205(C), pages 976-990.
    2. Tejeda De La Cruz, Alberto & Riviere, Philippe & Marchio, Dominique & Cauret, Odile & Milu, Anamaria, 2017. "Hardware in the loop test bench using Modelica: A platform to test and improve the control of heating systems," Applied Energy, Elsevier, vol. 188(C), pages 107-120.
    3. Menegon, Diego & Persson, Tomas & Haberl, Robert & Bales, Chris & Haller, Michel, 2020. "Direct characterisation of the annual performance of solar thermal and heat pump systems using a six-day whole system test," Renewable Energy, Elsevier, vol. 146(C), pages 1337-1353.

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