Accurate prediction of different forecast horizons wind speed using a recursive radial basis function neural network

M Madhiarasan

doi:10.1186/s41601-020-00166-8

Abstract

Environmental considerations have prompted the use of renewable energy resources worldwide for reduction of greenhouse gas emissions. An accurate prediction of wind speed plays a major role in environmental planning, energy system balancing, wind farm operation and control, power system planning, scheduling, storage capacity optimization, and enhancing system reliability. This paper proposes an accurate prediction of wind speed based ona Recursive Radial Basis Function Neural Network (RRBFNN) possessing the three inputs of wind direction, temperature and wind speed to improve modern power system protection, control and management. Simulation results confirm that the proposed model improves the wind speed prediction accuracy with least error when compared with other existing prediction models.

Highlights

Environmental degradation and depletion of conventional energy have helped direct attention towards wind energy
This paper proposes an accurate wind speed prediction model based on a Recursive Radial Basis Function Neural Network (RRBFNN)
3 Evaluation metrics The performance of the proposed wind speed prediction model is analyzed based on the statistical error criteria such as Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Relative Error (MRE) and Mean Absolute Percentage Error (MAPE) criteria

Summary

Introduction

Environmental degradation and depletion of conventional energy have helped direct attention towards wind energy. Anurag More et al [2], proposed cascade correlation and back propagation algorithms-based neural networks for short-term wind speed prediction. This paper proposes an accurate wind speed prediction model based on a Recursive Radial Basis Function Neural Network (RRBFNN).

Results

Conclusion