Publicação
Enhanced random vector functional link networks with bayesian-based hyperparameter optimization for wind speed forecasting
| authorProfile.email | biblioteca@isel.pt | |
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | |
| dc.contributor.author | Seman, Laio Oriel | |
| dc.contributor.author | Klaar, Anne Carolina Rodrigues | |
| dc.contributor.author | Ribeiro, Matheus Henrique Dal Molin | |
| dc.contributor.author | Stefenon, Stefano Frizzo | |
| dc.date.accessioned | 2026-01-05T12:11:20Z | |
| dc.date.available | 2026-01-05T12:11:20Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Accurate short-term wind speed forecasting is essential for reliable and efficient wind energy integration. This paper introduces an enhanced Random Vector Functional Link (RVFL) network optimized through a Bayesian-based Neural Architecture Search (NAS) framework. The proposed RVFL-OptBayes model incorporates multi-scale feature generation, including kernel approximations, Nystr & ouml;m sampling, Fastfood transforms, wavelet scattering, and Neural Tangent Kernel embeddings with Principal Component Analysis (PCA)-aligned orthogonal initializations and spectral normalization to improve stability and feature diversity. Experiments were conducted on real-world Brazilian wind farm data to evaluate forecasting performance. Results show that RVFL-OptBayes outperforms conventional RVFL networks, deep learning models, and ensemble methods, achieving an R2 above 0.99. The proposed framework demonstrates that lightweight randomized architectures, when combined with principled hyperparameter search, can rival or surpass complex deep learning models for time-series forecasting. The findings suggest strong potential for practical deployment in renewable energy systems, offering accurate and computationally efficient wind speed predictions to support operational planning, grid stability, and smart energy management. | eng |
| dc.identifier.citation | Seman, L. O., Klaar, A. C. R., Ribeiro, M. H. D., & Stefenon, S. F. (2025). Enhanced random vector functional link networks with bayesian-based hyperparameter optimization for wind speed forecasting. IEEE Access, 13, 208105-208122. https://doi.org/10.1109/ACCESS.2025.3640434 | |
| dc.identifier.doi | 10.1109/access.2025.3640434 | |
| dc.identifier.uri | http://hdl.handle.net/10400.21/22428 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
| dc.relation.hasversion | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11278055&utm_source=clarivate&getft_integrator=clarivate&tag=1 | |
| dc.relation.ispartof | IEEE Access | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Differentiable neural architecture | |
| dc.subject | Neural network architectures | |
| dc.subject | Predictive maintenance | |
| dc.subject | Vibration | |
| dc.subject | Forecasting | |
| dc.subject | Anomaly detection | |
| dc.title | Enhanced random vector functional link networks with bayesian-based hyperparameter optimization for wind speed forecasting | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 208122 | |
| oaire.citation.startPage | 208105 | |
| oaire.citation.title | IEEE Access | |
| oaire.citation.volume | 13 | |
| oaire.version | http://purl.org/coar/version/c_be7fb7dd8ff6fe43 |