Mathematical and functional link convolutional neural network model for optimum design parameter of solar chimney power plant.

Energy is essential to sustain life and development and to fulfill daily requirements. A clean source of energy like the Solar Chimney Power Plant (SCPP) is essential or hybridized with conventional sources to meet the demand. Also, it will provide energy during the night and it will reduce the need and expense of physical batteries in comparison to the photovoltaic energy storage system. In this paper, a mathematical study has been done for the generation of 10 MW power from the SCPP. This paper optimized the core parameter of SCPP such as chimney height and collector radius under different operating conditions. It has been evaluated on the basis of data produced by the developed mathematical model. The current research work highlights the modern application of state‐of‐the‐art Deep Learning (DL) techniques that is, Functional Link Convolutional Neural Network (FLCNN) to map developed mathematical models. The proposed FLCNN is compared with various other Machine Learning algorithms including Support Vector Regression, K‐Nearest Neighbors, Random Forest, Naïve Bayes, and Multilayer Perceptron. Proposed FLCNN performed efficiently and achieved 91% and 93% of Adjusted R2 score for predicting values of collector radius and chimney height, respectively, which is comparable to other ML algorithms. [ABSTRACT FROM AUTHOR]