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The increasing energy demand and the environmental concerns of fossil fuels have looked for sustainable alternatives more desperately than ever. Traditional Solar Chimney Power Plants are a promising solution because they are low cost and low maintenance, but their low energy conversion efficiency restricts them. A novel hybrid strategy using transparent photovoltaic cells and a solar chimney desalination method is presented to address this issue and enhance power generation. Concurrently implementing the Snooker Optimization Algorithm (SBOA) and Patient Adversarial Neural Network (PANN) is the proposed hybrid approach. Thus, it is named SBOA-PANN strategy. The primary objectives are to increase the solar chimney power plant's (SCPP) energy efficiency in both generating and desalination. The SBOA algorithm maximizes energy generation by optimizing factors such as collector area and chimney height. The PANN is employed to forecast the desalination rates under different operational conditions. The new technique is evaluated and compared with other current techniques such as the Bees Algorithm (BA), Multi-objective Grasshopper Optimization Algorithm (MOGA), and non-dominated sorting genetic algorithm (NSGA) based on the MATLAB platform. Results show an increase in energy output by 30% while achieving a peak power generation of 1.25 MW, and a 27% increase in freshwater output to 500 liters per hour. The PANN model obtained a prediction accuracy of 96%. This work contributes to solar energy technology progress by enhancing the exploitation of renewable resources and the general efficiency of solar chimney systems.
B. T. Geetha (Tue,) studied this question.