Multi-Step-Ahead Wind Speed Forecast System: Hybrid Multivariate Decomposition and Feature Selection-Based Gated Additive Tree Ensemble Model

被引：2
|
作者：
Joseph, Lionel P. [1 ,2 ]
Deo, Ravinesh C. [1 ,2 ]
Casillas-Perez, David [3 ]
Prasad, Ramendra [4 ]
Raj, Nawin [1 ]
Salcedo-Sanz, Sancho [5 ]
机构：
[1] Univ Southern Queensland, Sch Math Phys & Comp, Springfield, Qld 4300, Australia
[2] Univ Southern Queensland, Ctr Appl Climate Sci, Toowoomba, Qld 4350, Australia
[3] Univ Rey Juan Carlos, Dept Signal Proc & Commun, Madrid 28942, Spain
[4] Univ Fiji, Sch Sci & Technol, Dept Sci, Lautoka, Fiji
[5] Univ Alcala, Dept Signal Proc & Commun, Madrid 28805, Spain
来源：
IEEE ACCESS | 2024年 / 12卷
关键词：
Forecasting; Long short term memory; Predictive models; Logic gates; Computational modeling; Autoregressive processes; Wind power generation; Velocity measurement; Wind speed; Whale optimization algorithms; Bayes methods; Wind speed forecasting; gated additive tree ensemble; multivariate empirical mode decomposition; opposition-based whale optimization algorithm; Bayesian optimization; WHALE OPTIMIZATION ALGORITHM; GLOBAL SOLAR-RADIATION; RECURRENT UNIT NETWORK; WAVELET TRANSFORM; NEURAL-NETWORKS; PREDICTION; NOISE;
D O I：
10.1109/ACCESS.2024.3392899
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 t(L+1) ), 8.67 - 14.07% (at t(L+2 )), and 11.60 - 18.37% (at t(L+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.
引用
收藏
页码：58750 / 58777
页数：28
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