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E. Rajasekaran
VSB Engineering College

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Weather prediction using random forest machine learning model R. Meenal; Prawin Angel Michael; D. Pamela; E. Rajasekaran
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp1208-1215

Abstract

The complex numerical climate models pose a big challenge for scientists in weather predictions, especially for tropical system. This paper is focused on presenting the importance of weather prediction using machine learning (ML) technique. Recently many researchers recommended that the machine learning models can produce sensible weather predictions in spite of having no precise knowledge of atmospheric physics. In this work, global solar radiation (GSR) in MJ/m2/day and wind speed in m/s is predicted for Tamil Nadu, India using a random forest ML model. The random forest ML model is validated with measured wind and solar radiation data collected from IMD, Pune. The prediction results based on the random forest ML model are compared with statistical regression models and SVM ML model. Overall, random forest machine learning model has minimum error values of 0.750 MSE and R2 score of 0.97. Compared to regression models and SVM ML model, the prediction results of random forest ML model are more accurate. Thus, this study neglects the need for an expensive measuring instrument in all potential locations to acquire the solar radiation and wind speed data.
Review on mathematical models for the prediction of solar radiation Meenal Rajasekaran; A.Immanuel Selvakumar; E. Rajasekaran
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 1: July 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i1.pp56-61

Abstract

Global Solar Radiation (GSR) data is important for all solar energy based applications, mainly to forecast the output power of solar PV system in case of renewable energy integration in to the existing grid. The solar radiation components are measured using pyranometer, solarimeter, pyroheliometer and so on. It is not practically possible to install this radiation measuring instruments at all the locations due to the cost and difficulty in measurements. Hence the availability of solar radiation data is limited to few meteorological stations especially in the developing country like India. Therefore, it is necessary to develop mathematical models to predict the solar radiation to eliminate the costly pyranometer. In this paper, the review of mathematical models using trigonometric functions for the prediction of global solar radiation is presented. The mathematical models are applicable wherever the radiation data is unavailable. From the review results, it is concluded that mathematical model with both sine and cosine wave equation gives good prediction accuracy with correlation coefficient of 0.95
Co-Authors A.Immanuel Selvakumar D. Pamela Meenal Rajasekaran Prawin Angel Michael R. Meenal