Predicting Performance of Solar updraught Tower using Machine Learning Regression Model

Abstract

ABSTRACT This work aims to analyse the performance of the SUT power plant in the environmental conditions of Nagpur and develop machine learning (ML) model to predict the power output of the SUT power plant. The developed ML model readily predicts the power output without rigorous calculations. Regularised polynomial regression with multiple variables technique was used to develop the ML model. Optimum values of the regularisation parameter, degree of the polynomial and the number of training examples were obtained from learning curves. A mathematical model of the SUT power plant is developed for analysing the performance of the SUT power plant and collecting training datasets for the ML regression model. The mathematical model analysed the effect of variation in chimney height, chimney and collector radius on SUT’s performance and flow parameters. The results demonstrate that raising the height and radius of the chimney and collector radius enhances the power generation of the SUT power plant. The monthly average power output was observed to be maximum in June and July and minimum in December. Calculation of the power output is done from 5.00 AM to 6 PM. The maximum power production in June is 120 kW and in May is 117 kW. The average annual power output is around 64 kW.