Random Scaling-Based Bat Algorithm for Greenhouse Thermal Model Identification and Experimental Validation

In this paper, we propose a Random Scaling-based Bat Algorithm (RSBA) for parametric identification of a greenhouse thermal model. The proposition includes modifying the exploitation of the standard BA by making the scaling parameter changes randomly over the iterations. The proposed thermal model identification method has been assessed first on a simulated greenhouse thermal model with known parameters. The simulation results have shown the superiority of the proposed RSBA compared to the standard BA in term of convergence and performance accuracy. To experimentally investigate the proposed identification method, we used a greenhouse prototype under arid climate conditions located in M’ziraa, Biskra, Algeria. The obtained prediction results are found to be in a good agreement with the measured ones which show the effectiveness of the proposed RSBA in identifying the real greenhouse thermal model.