Multi-Step-Ahead Wind Speed Forecast System: Hybrid Multivariate Decomposition and Feature Selection-Based Gated Additive Tree Ensemble Model
Article
Article Title | Multi-Step-Ahead Wind Speed Forecast System: Hybrid Multivariate Decomposition and Feature Selection-Based Gated Additive Tree Ensemble Model |
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ERA Journal ID | 210567 |
Article Category | Article |
Authors | Joseph, Lionel P., Deo, Ravinesh C., Casillas-Perez, David, Prasad, Ramendra, Raj, Nawin and Salcedo-sanz, Sancho |
Journal Title | IEEE Access |
Journal Citation | 12, pp. 58750-58777 |
Number of Pages | 28 |
Year | 2024 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 2169-3536 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2024.3392899 |
Web Address (URL) | https://ieeexplore.ieee.org/abstract/document/10507841 |
Abstract | Wind, being a clean and sustainable resource, boasts environmental advantages. However, its electricity generation faces challenges due to unpredictable variations in wind speed (WS). Accurate predictions of these variations would allow mixed grids to adjust their energy mix in real-time, ensuring overall stability. For this purpose, the paper develops a new hybrid gated additive tree ensemble (H-GATE) model for accurate multi-step-ahead WS predictions. First, the multivariate empirical mode decomposition (MEMD) simultaneously demarcates the multivariate data into intrinsic mode functions (IMFs) and residuals. These components represent underlying trends, periodicity, and stochastic patterns in WS variations. The IMF and residual components are pooled in respective sets, and an opposition-based whale optimization algorithm (OBWOA) is applied for dimensionality reduction. The selected features are used by GATE tuned with Bayesian optimization (BO) to forecast the individual IMF and residual components. The outputs are summed to obtain the final multi-step-ahead WS forecasts. The proposed H-GATE is benchmarked against standalone (S-GATE, S-CLSTM, and S-ABR) and hybrid (H-CLSTM and H-ABR) models. Based on all statistical metrics and diagnostic plots, H-GATE outperforms all comparative models at all forecast horizons, accumulating the lowest mean absolute percentage error (MAPE) of 6.13 - 9.93% (at tL+1 ), 8.67 - 14.07% (at tL+2 ), and 11.60 - 18.37% (at tL+3 ) across all three sites. This novel multi-step-ahead WS forecasting strategy can significantly benefit grid operators by helping anticipate fluctuations in wind power generation. This can assist in optimizing energy dispatch schedules, reducing reliance on backup power sources, and enhancing overall grid stability. Practical implementation of this method can help meet the rising energy demands through renewable wind energy. |
Related Output | |
Is part of | Advancing Stochastic Wind Speed Forecasting Methods with Novel Hybrid Deep Learning Techniques |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
Byline Affiliations | School of Mathematics, Physics and Computing |
Centre for Applied Climate Sciences | |
Rey Juan Carlos University, Spain | |
University of Fiji, Fiji |
https://research.usq.edu.au/item/z796y/multi-step-ahead-wind-speed-forecast-system-hybrid-multivariate-decomposition-and-feature-selection-based-gated-additive-tree-ensemble-model
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