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Feature selection in wind speed forecasting systems based on meta-heuristic optimization
Technology for anticipating wind speed can improve the safety and stability of power networks with heavy wind penetration. Due to the unpredictability and instability of the wind, it is challenging to accurately forecast wind power and speed. Several approaches have been developed to improve this ac...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9904490/ https://www.ncbi.nlm.nih.gov/pubmed/36749744 http://dx.doi.org/10.1371/journal.pone.0278491 |
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author | El-kenawy, El-Sayed M. Mirjalili, Seyedali Khodadadi, Nima Abdelhamid, Abdelaziz A. Eid, Marwa M. El-Said, M. Ibrahim, Abdelhameed |
author_facet | El-kenawy, El-Sayed M. Mirjalili, Seyedali Khodadadi, Nima Abdelhamid, Abdelaziz A. Eid, Marwa M. El-Said, M. Ibrahim, Abdelhameed |
author_sort | El-kenawy, El-Sayed M. |
collection | PubMed |
description | Technology for anticipating wind speed can improve the safety and stability of power networks with heavy wind penetration. Due to the unpredictability and instability of the wind, it is challenging to accurately forecast wind power and speed. Several approaches have been developed to improve this accuracy based on processing time series data. This work proposes a method for predicting wind speed with high accuracy based on a novel weighted ensemble model. The weight values in the proposed model are optimized using an adaptive dynamic grey wolf-dipper throated optimization (ADGWDTO) algorithm. The original GWO algorithm is redesigned to emulate the dynamic group-based cooperative to address the difficulty of establishing the balance between exploration and exploitation. Quick bowing movements and a white breast, which distinguish the dipper throated birds hunting method, are employed to improve the proposed algorithm exploration capability. The proposed ADGWDTO algorithm optimizes the hyperparameters of the multi-layer perceptron (MLP), K-nearest regressor (KNR), and Long Short-Term Memory (LSTM) regression models. A dataset from Kaggle entitled Global Energy Forecasting Competition 2012 is employed to assess the proposed algorithm. The findings confirm that the proposed ADGWDTO algorithm outperforms the literature’s state-of-the-art wind speed forecasting algorithms. The proposed binary ADGWDTO algorithm achieved average fitness of 0.9209 with a standard deviation fitness of 0.7432 for feature selection, and the proposed weighted optimized ensemble model (Ensemble using ADGWDTO) achieved a root mean square error of 0.0035 compared to state-of-the-art algorithms. The proposed algorithm’s stability and robustness are confirmed by statistical analysis of several tests, such as one-way analysis of variance (ANOVA) and Wilcoxon’s rank-sum. |
format | Online Article Text |
id | pubmed-9904490 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99044902023-02-08 Feature selection in wind speed forecasting systems based on meta-heuristic optimization El-kenawy, El-Sayed M. Mirjalili, Seyedali Khodadadi, Nima Abdelhamid, Abdelaziz A. Eid, Marwa M. El-Said, M. Ibrahim, Abdelhameed PLoS One Research Article Technology for anticipating wind speed can improve the safety and stability of power networks with heavy wind penetration. Due to the unpredictability and instability of the wind, it is challenging to accurately forecast wind power and speed. Several approaches have been developed to improve this accuracy based on processing time series data. This work proposes a method for predicting wind speed with high accuracy based on a novel weighted ensemble model. The weight values in the proposed model are optimized using an adaptive dynamic grey wolf-dipper throated optimization (ADGWDTO) algorithm. The original GWO algorithm is redesigned to emulate the dynamic group-based cooperative to address the difficulty of establishing the balance between exploration and exploitation. Quick bowing movements and a white breast, which distinguish the dipper throated birds hunting method, are employed to improve the proposed algorithm exploration capability. The proposed ADGWDTO algorithm optimizes the hyperparameters of the multi-layer perceptron (MLP), K-nearest regressor (KNR), and Long Short-Term Memory (LSTM) regression models. A dataset from Kaggle entitled Global Energy Forecasting Competition 2012 is employed to assess the proposed algorithm. The findings confirm that the proposed ADGWDTO algorithm outperforms the literature’s state-of-the-art wind speed forecasting algorithms. The proposed binary ADGWDTO algorithm achieved average fitness of 0.9209 with a standard deviation fitness of 0.7432 for feature selection, and the proposed weighted optimized ensemble model (Ensemble using ADGWDTO) achieved a root mean square error of 0.0035 compared to state-of-the-art algorithms. The proposed algorithm’s stability and robustness are confirmed by statistical analysis of several tests, such as one-way analysis of variance (ANOVA) and Wilcoxon’s rank-sum. Public Library of Science 2023-02-07 /pmc/articles/PMC9904490/ /pubmed/36749744 http://dx.doi.org/10.1371/journal.pone.0278491 Text en © 2023 El-kenawy et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article El-kenawy, El-Sayed M. Mirjalili, Seyedali Khodadadi, Nima Abdelhamid, Abdelaziz A. Eid, Marwa M. El-Said, M. Ibrahim, Abdelhameed Feature selection in wind speed forecasting systems based on meta-heuristic optimization |
title | Feature selection in wind speed forecasting systems based on meta-heuristic optimization |
title_full | Feature selection in wind speed forecasting systems based on meta-heuristic optimization |
title_fullStr | Feature selection in wind speed forecasting systems based on meta-heuristic optimization |
title_full_unstemmed | Feature selection in wind speed forecasting systems based on meta-heuristic optimization |
title_short | Feature selection in wind speed forecasting systems based on meta-heuristic optimization |
title_sort | feature selection in wind speed forecasting systems based on meta-heuristic optimization |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9904490/ https://www.ncbi.nlm.nih.gov/pubmed/36749744 http://dx.doi.org/10.1371/journal.pone.0278491 |
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