![]() Li G, Shi J (2010) On comparing three artificial neural networks for wind speed forecasting. Electronics and Computer Applications (ICPECA), IEEE, pp 876–882 ![]() 2023 IEEE 3rd International Conference on Power. Jiang B, Liu Y, Xie H (2023) Super short-term wind speed prediction based on ceemd decomposition and bilstm-transformer model. ![]() Jiang P, Liu Z, Wang J, Zhang L (2021) Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm. Huang S-C, Chiou C-C, Chiang J-T, Wu C-F (2020) A novel intelligent option price forecasting and trading system by multiple kernel adaptive filters. Gani A (2021) Fossil fuel energy and environmental performance in an extended stirpat model. ĭuan J, Zuo H, Bai Y, Duan J, Chang M, Chen B (2021) Short-term wind speed forecasting using recurrent neural networks with error correction. J Renewable and Sustain Energy 15(2):026101ĭragomiretskiy K, Zosso D (2014) Variational mode decomposition. Energy 265:126383īonventi W Jr, Godoy EP (2021) Fuzzy logic for renewable energy recommendation and regional consumption forecast using sarima and lstm. Environ Sci Pollut Res 1–13īommidi BS, Teeparthi K, Kosana V (2023) Hybrid wind speed forecasting using iceemdan and transformer model with novel loss function. Appl Energy 333:120565īommidi BS, Kosana V, Teeparthi K, Madasthu S (2023) Hybrid attention-based temporal convolutional bidirectional lstm approach for wind speed interval prediction. ![]() Alex Eng J 74:51–63īentsen LØ, Warakagoda ND, Stenbro R, Engelstad P (2023) Spatio-temporal wind speed forecasting using graph networks and novel transformer architectures. Sample Convolution and Interaction Network SSA:ġ-Dimensional Convolutional Neural NetworkĪl-Duais FS, Al-Sharpi RS (2023) A unique markov chain monte carlo method for forecasting wind power utilizing time series model. Seasonal Autoregressive Moving Average SCINet: Kernel Method Based Support Vector Regression k-SVR: The results from two experiments demonstrate that the proposed approach outperforms other models, leading to a significant improvement in WSF accuracy across all evaluated time intervals.Īutoregressive Integrated Moving Average ARMA:Ĭomplete Ensemble Empirical Mode Decomposition DLM:Įnsemble Empirical Mode Decomposition ELM: To evaluate the WSF capability of the proposed hybrid model, it’s performance is compared to robust models using data from two wind farms across six different time horizons such as 5-min, 10-min, 15-min, 30-min, 1-hour, and 2-hours. The wind speed data acquired from two distinct sites: Leicester, and Portland is used for the evaluation. This study utilizes VMD as a denoising technique for wind speed data and incorporates SCINet to capture global patterns and long-range dependencies for the WSF. Hence, this study introduces a novel approach (VMD-SCINet) for wind speed forecasting (WSF) by integrating the strengths of variational mode decomposition (VMD) and sample convolution and interaction network (SCINet) architecture for the prediction of wind speed. However, the variability and stochastic nature of wind speed makes accurate forecasting difficult. The VMD–EEMD–LSTM model exhibited significantly improved predictive performance.Wind energy is gaining importance owing to its renewable and environmentally friendly characteristics. In comparison with the EEMD–LSTM model, the RMSE decreased by an average of 26.95%, the MAE decreased by an average of 28.00%, and the R2 increased by an average of 6.53%. The RMSE (root mean square error) decreased by an average of 58.68%, the MAE (mean absolute error) reduced by an average of 59.96%, and the R2 (coefficient of determination) increased by an average of 49.85% compared with the VMD–LSTM model. The experimental results demonstrated significant improvements in the predictive performance compared with the VMD–LSTM model. These IMFs decomposed by VMD and EEMD are utilized as features in the LSTM model for making predictions, culminating in the final forecasted results. EEMD further dissects the residual sequence obtained from VMD into second-order components. This model decomposes satellite altimetry data from near the Dutch coast using VMD, resulting in components of the Intrinsic Mode Function (IMF) with various frequencies and a residual sequence. To enhance the accuracy of the predictions of changes in sea level, this study introduced an improved VMD–EEMD–LSTM hybrid model. Changes in sea level exhibit nonlinearity, nonstationarity, and multivariable characteristics, making traditional time series forecasting methods less effective in producing satisfactory results.
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