Analysis for the prediction of solar and wind generation in India using ARIMA, linear regression and random forest algorithms. Issue 2 (April 2023)

• Record Type: Journal Article
• Authors: Chauhan, Brajlata; Tabassum, Rashida; Tomar, Sanjiv; Pal, Amrindra
• Is Part Of: Wind engineering. Volume 47:Issue 2(2023)
• Page Start: 251
• Page End: 265
• Publication Date: 2023-04
• Subjects: Renewable energy -- solar energy -- wind energy -- machine learning -- linear regression -- random forest -- time series -- ARIMA -- MAE -- MSE -- RMSE -- MAPE; Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X221126742
• Languages: English
• ISSNs: 0309-524X

Abstract

This work focused on the prediction of generation of renewable energy (solar and wind) using the machine learning ML algorithms. Prediction of generation are very important to design the better microgrids storage. The various ML algorithms are as logistic regression LR and random forest RA and the ARIMA, time series algorithms. The performance of each algorithm is evaluated using the mean absolute error, mean squared error, root mean squared error, and mean absolute percentage error. The MAE value for the ARIMA (0.06 and 0.20) model for solar and wind energy is very less as compared to RF (15.65 and 61.73) and LR (15.78 and 54.65) of solar and wind energy. Same with MSE and RMSE, the MSE and RMSE value for the ARIMA of solar energy model obtained is 0.01 and 0.08 and wind energy is 0.07 and 0.27 respectively. Comparative analysis of all of these matrices of each algorithm for both the dataset, we concluded that the ARIMA model is best fit for the forecasting of solar energy and wind energy.