Predication of air velocity in Solar Chimney using RBFNN

Solar Chimney plays an important role to improve photovoltaic (PV) system efficiency against rising in operating temperature. In this paper, predication of maximum air velocity in Solar Chimney (SC) using RBFNN was proposed. First, a brief description of theoretical solar cooling chimney module and discusses the effect it´s parameter on the air flow velocity. Theoretical analysis used to generate learning data by using standard solar panels integrated with 40 SC modules with varying PV energy. The RBFNN model has 4 input nodes representing the input layers is made 4 nodes chimney height Hc, Width Wc, thickness tc and wall temperature Tsa and one output node represented by maximum air flow velocity. Further the temperature drop in the photovoltaic panel is also estimated based on predicted air velocities. Simulation result shows the predicted air flow velocity inside solar chimney closely match with the analytical data.