Ultra-Short-Term Wind Power Prediction by Salp Swarm Algorithm-Based Optimizing Extreme Learning Machine

Lian Tan,Hongtao Zhang,Jing Han

doi:10.1109/access.2020.2978098

Abstract

Wind power generation accounts for an increasing proportion of the power grid, so efficient and accurate real-time wind power prediction is particularly important for wind power grid. In view of the strong randomness and fluctuation of wind and the difficulty of predicting wind power, a Salp Swarm Algorithms-Extremely Learning Machine (SSA-ELM) based ultra-short-term wind power prediction model is proposed. In this case, the multi-input sample set is composed of historical wind speed, temperature, wind direction, atmospheric pressure and other climatic factors that are highly correlated with wind power, and the network parameters are determined in the training process. In order to improve the adaptability and accuracy of the prediction model, the input weight matrix and hidden layer deviation of the Extreme Learning Machine (ELM) are optimized by exploring and developing the Salp Swarm Algorithm in the iterative process. Finally, the simulation experiment is conducted with the actual data of a wind farm in Henan Province, and the comparison with the traditional Extreme Learning Machine, Particle Swarm Optimization Extreme Learning Machine (PSO-ELM) and Back Propagation (BP) neural network model shows that the new method avoids falling into the local extreme value, and has faster convergence speed and higher prediction accuracy.

Highlights

Wind power, which is environmental-friendly and is abundant in nature, plays an important role in the development of human society
The corresponding histogram of the data comparison between 894 and 1788 samples in January, April, July and October for Salp Swarm Algorithms-Extremely Learning Machine (SSA-Extreme Learning Machine (ELM)), Particle Swarm Optimization Extreme Learning Machine (PSO-ELM), ELM and Back Propagation (BP) model prediction errors is drawn. It can be intuitively seen from the figure 7, 8 and 9, compared with the small sample size used for training, verification and testing, the larger sample size will lead to a slight increase in the values of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) generated by the neural network during the prediction
The results showed that compared with the other three models, Salp Swarm Algorithm (SSA)-ELM had higher prediction accuracy and stronger ability to track the actual wind power

Summary

INTRODUCTION

Wind power, which is environmental-friendly and is abundant in nature, plays an important role in the development of human society. Networks (SLFNs) [2], the wavelet decomposition algorithm [3], [4], the empirical mode decomposition [5] and the other algorithms combined with traditional neural networks (such as BP neural network, Support Vector Machine, etc.) can process and predict wind power time series data Most of these methods have complex parameters and weak generalization ability, so it is difficult to obtain the optimal prediction effect. Extreme Learning Machine (ELM) is one kind of single hidden layer forward neural network, which has the advantages of strong generalization ability, simple structure and easy training. SSA is used to optimize the input connection weights and implicit bias of the single hidden feed forward neural network for ELM, which improves the generalization ability of ELM and avoids its over-fitting problem. The performance of ELM has been greatly improved, and its application scope has been increasingly wide

SALP SWARM ALGORITHM

THE EXAMPLE ANALYSIS

Findings

CONCLUSION