Short-term wind speed prediction based on CEEMDAN-SE-improved PIO-GRNN model

Jiale Ding,Yongmin Huang,Kuo Yuan,Haohao Xu,Zhiquan Zhu,Guochu Chen

doi:10.1177/0020294020981400

Abstract

In this paper, a short-term wind speed prediction model, called CEEMDAN-SE-Improved PIO-GRNN, is proposed to optimize the accuracy of the short-term wind speed forecast. This model is established on account of the optimized General Regression Neural Network (GRNN) method optimized by three algorithms, which are Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Sample Entropy (SE), and Pigeon Inspired Optimization (PIO), separately. Firstly, decomposing the original wind speed sequences into several subsequences with different complexity by CEEMDAN. Then, the complexity of each subsequence is judged by SE and the similar subsequences are combined into a new sequence to reduce the scale of calculation. Afterwards, the GRNN model optimized by improved PIO is used to predict each new sequence. Finally, the predicted results are superposed as the eventual predicted value. Implementing the prediction for the wind speed data of a wind field in north China within 30 days by applying the different prediction models, namely, GRNN, CEEMDAN-GRNN, Improved PIO-GRNN, and CEEMDAN-SE-Improved PIO-GRNN which are proposed in this paper. Comparing the prediction curves of different models with the fitting curve of the actual wind speed shows that the optimal fitting effect and minimum error value are included in CEEMDAN-SE-Improved PIO-GRNN model. Specifically, the values of mean squared error (MSE), mean absolute error (MAE) and weighted mean absolute percentage error (WMAPE) separately decrease by 0.6222, 0.3334, and 8.5766%, which compare with the single prediction model GRNN. Meanwhile, diebold-mariano (DM) test shows that the prediction ability of the two models is significantly different. The above statements indicate the proposed model does great advance in the precision of short-term wind speed prediction.

Highlights

Time in recent decade, with the rapid deterioration of the global climate, the wind power, a kind of renewable clean energy, is gradually developed and utilized in many countries and cities because of its advantages of abundant reserves and wide distribution region.[1,2] By the end of 2019, Europe’s cumulative installed capacity of wind power is 205 GW, accounting for 15% of Europe’s electricity demand.[3]
The mean squared error (MSE), mean absolute error (MAE) and weighted mean absolute percentage error (WMAPE) of CEEMDAN-General Regression Neural Network (GRNN) are respectively 0.3609, 0.1512, and 3.8901% lower than that of GRNN, and the prediction ability difference is significant, which indicates that CEEMDAN decomposition can effectively reduce the interaction effect between various frequency components, thereby effectively improving the prediction accuracy
Results in the MSE meaning, it indicates that the prediction ability of CEEMDAN-Sample Entropy (SE)-GRNN is stronger than that of CEEMDAN-GRNN at the 20% significant level

Summary

Introduction

With the rapid deterioration of the global climate, the wind power, a kind of renewable clean energy, is gradually developed and utilized in many countries and cities because of its advantages of abundant reserves and wide distribution region.[1,2] By the end of 2019, Europe’s cumulative installed capacity of wind power is 205 GW, accounting for 15% of Europe’s electricity demand.[3]. Aiming at the problem of strong fluctuation of wind speed data in north China wind field, Complete Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm[15] was adopted to decompose the wind speed sequence with strong instability into a series of eigenmode functions to reduce its fluctuation It is widely used in processing nonlinear and nonstationary signals because it synchronously overcomes the problem of EMD mode aliasing and the difficulty of determining the amplitude of Gaussian white noise in EEMD decomposition. The specific implementation steps are as follows: 1) To carry out the stabilization of the wind speed sequence, the original wind speed sequence is decomposed by CEEMDAN technology to generate a series of IMF components with disparate characteristic scales. The IMF components with similar entropy are combined to form a new component, which reduces the calculation scale of the prediction model and the error accumulation

79 White Noise

50 IMF8 0

Conclusion