Design of Neural Network Based Wind Speed Prediction Model Using GWO

R. Kingsy Grace,R. Manimegalai

doi:10.32604/csse.2022.019240

Abstract

The prediction of wind speed is imperative nowadays due to the increased and effective generation of wind power. Wind power is the clean, free and conservative renewable energy. It is necessary to predict the wind speed, to implement wind power generation. This paper proposes a new model, named WT-GWO-BPNN, by integrating Wavelet Transform (WT), Back Propagation Neural Network (BPNN) and Grey Wolf Optimization (GWO). The wavelet transform is adopted to decompose the original time series data (wind speed) into approximation and detailed band. GWO – BPNN is applied to predict the wind speed. GWO is used to optimize the parameters of back propagation neural network and to improve the convergence state. This work uses wind power data of six months with 25, 086 data points to test and verify the performance of the proposed model. The proposed work, WT-GWO-BPNN, predicts the wind speed using a three-step procedure and provides better results. Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean absolute percentage error (MAPE) and Root mean squared error (RMSE) are calculated to validate the performance of the proposed model. Experimental results demonstrate that the proposed model has better performance when compared to other methods in the literature.

Highlights

The rapid growth of the world economy, the renewable energy sources such as solar, tidal, wind and geothermal energy has significantly shown its importance around the globe
This paper proposes a new model, named WT-GWO-BPNN, by integrating Wavelet Transform (WT), Back Propagation Neural Network (BPNN) and Grey Wolf Optimization (GWO)
This section deals with the experimental results obtained using the proposed system WT-GWO-BPNN. 25,086 data points are chosen as input for prediction

Summary

Introduction

The rapid growth of the world economy, the renewable energy sources such as solar, tidal, wind and geothermal energy has significantly shown its importance around the globe. The amount of wind around the wind farm must be estimated to forecast the wind power. Wind speed is an important factor that affects the wind power. The other factors include location of wind farm and weather. Numerical-Weather-Predictions [2] are based on physical methods and the statistical methods. They are based on the historical data and not on the meteorological data such as temperature, pressure, surface conditions and obstacles. Time series data are used for the estimation of wind speed in statistical methods along with artificial intelligence

Methods

Results

Conclusion