A Data Pre-Processing-BFGS Machine Learning Model for Forecasting Solar Irradiation

A Data Pre-Processing-BFGS  Machine Learning Model for Forecasting Solar Irradiation

Kapil Raykol1 Prof. C.K.Tiwari2  Prof.Anil Malviya3

M.Tech Scholor, Patel College of Science and Technology, Indore1

Associate Professor, Patel College of Science and Technology, Indore2

Assistant Professor, Patel College of Science and Technology, Indore3

Abstract— Artificial intelligence and machine learning have made its presence felt ubiquitously in different avenues of research and technology wherein the data is large and complex. In the proposed work, to forecast solar irradiation energy; whose structure uses the back-propagation algorithm is used. The averaging  window approach is also been used so as to increase the accuracy of prediction. The performance metrics which have been evaluated are the gradient, combination co-efficients, mean squared error and mean absolute percentage error. The system attains a MAPE of 1.77%. Hence the accuracy attained is 98.23%. The mean square error has been chosen as the performance function for the proposed algorithm.

Keywords—Solar Energy Prediction, Back Propagation, Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Regression.