Version 1
: Received: 11 April 2024 / Approved: 11 April 2024 / Online: 12 April 2024 (04:53:42 CEST)
How to cite:
Sun, Y.; Zhang, S. A Multi-scale Hybrid Wind Power Prediction Model Based on LSSVR-RELM-Multi-Head Attention-BiGRU and Data Decomposition. Preprints2024, 2024040820. https://doi.org/10.20944/preprints202404.0820.v1
Sun, Y.; Zhang, S. A Multi-scale Hybrid Wind Power Prediction Model Based on LSSVR-RELM-Multi-Head Attention-BiGRU and Data Decomposition. Preprints 2024, 2024040820. https://doi.org/10.20944/preprints202404.0820.v1
Sun, Y.; Zhang, S. A Multi-scale Hybrid Wind Power Prediction Model Based on LSSVR-RELM-Multi-Head Attention-BiGRU and Data Decomposition. Preprints2024, 2024040820. https://doi.org/10.20944/preprints202404.0820.v1
APA Style
Sun, Y., & Zhang, S. (2024). A Multi-scale Hybrid Wind Power Prediction Model Based on LSSVR-RELM-Multi-Head Attention-BiGRU and Data Decomposition. Preprints. https://doi.org/10.20944/preprints202404.0820.v1
Chicago/Turabian Style
Sun, Y. and Shiyang Zhang. 2024 "A Multi-scale Hybrid Wind Power Prediction Model Based on LSSVR-RELM-Multi-Head Attention-BiGRU and Data Decomposition" Preprints. https://doi.org/10.20944/preprints202404.0820.v1
Abstract
Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Permutation Entropy(PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine(RELM), Multi-head Attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high, mid,and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Betele Optimizer(DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. Empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM,and MHA-BiGRU. In comparison to other benchmark models, the proposed model exhibits a reduction in Root Mean Square Error(RMSE) values of over 10%, conclusively affirming its superior predictive accuracy.
Keywords
wind power prediction; ICEEMDAN; Multi-head attention mechanism; BiGRU; LSSVR; RELM
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
