Abstract
Short-term wind speed forecasting is crucial to the utilization of wind energy, and it has been employed widely in turbine regulation, electricity market clearing, and preload sharing. However, the wind speed has inherent fluctuation, and accurate wind speed prediction is challenging. This paper aims to propose a hybrid forecasting approach of short-term wind speed based on a novel signal processing algorithm, a wrapper-based feature selection method, the state-of-art optimization algorithm, ensemble learning, and an efficient artificial neural network. Variational mode decomposition (VMD) is employed to decompose the original wind time-series into sublayer modes. The binary bat algorithm (BBA) is used to complete the feature selection. Bayesian optimization (BO) fine-tuned online sequential extreme learning machine (OSELM) is proposed to forecast the low-frequency sublayers of VMD. Bagging-based ensemble OSELM is proposed to forecast high-frequency sublayers of VMD. Two experiments were conducted on 10 min datasets from the National Renewable Energy Laboratory (NREL). The performances of the proposed model were compared with various representative models. The experimental results indicate that the proposed model has better accuracy than the comparison models. Among the thirteen models, the proposed VMD-BBA-EnsOSELM model can obtain the best prediction accuracy, and the mean absolute percent error (MAPE) is always less than 0.09.
Highlights
Wind energy has grown substantially for two decades [1]
In series 1, from the binary bat algorithm (BBA)-online sequential extreme learning machine (OSELM) model to the BOOSELM model, the mean absolute error (MAE) is reduced by 30.11%, the mean absolute percent error (MAPE) is reduced by 23.83%, and the root mean square error (RMSE) is reduced by 29.95%; from the particle swarm optimization (PSO)-OSELM model to GPR LSSVR LSTM OSELM AdaBoost-OSELM Bagging-OSELM BBA-OSELM Bayesian optimization (BO)-OSELM PSO-OSELM empirical mode decomposition (EMD)-BBA-OSELM ensemble empirical mode decomposition (EEMD)-BBA-OSELM Variational mode decomposition (VMD)-BBA-OSELM VMD-BBA-EnsOSELM
Short-term wind speed forecasting is significant to wind energy development, and it is widely applied to turbine regulation, electricity market clearing, and preload sharing. is paper has presented a novel hybrid forecasting method based on VMD, BBA, BO, Bagging, and OSELM
Summary
Wind energy has grown substantially for two decades [1]. It has become one of the primary renewable energy sources. Zhang et al [14] proposed an online sequential outlier robust extreme learning machine (OSORELM) for short-term wind speed prediction. A novel signal processing method, variational mode decomposition (VMD), was proposed to overcome the above obstacles It can break down the original wind speed timeseries into a set of band-limited sublayer modes named intrinsic mode functions (IMFs). Is paper proposes a novel approach for short-term wind speed prediction based on the above issues. Compared to EMD and its variants, the proposed approach is more robust to data noise (2) BBA is employed to complete the feature selection. E Bagging-OSELM reveals better stability and accuracy than OSELM and AdaBoost-OSELM e remaining part of the paper proceeds as follows: Section 2 introduces the proposed hybrid model, Section 3 presents the experimental results and discussion, and Section 4 draws the conclusions BO is used to optimize the structure of OSLEM (4) Bagging-based ensemble OSELM, referred to as Bagging-OSELM, is employed to forecast high-frequency sublayers of VMD. e Bagging-OSELM reveals better stability and accuracy than OSELM and AdaBoost-OSELM e remaining part of the paper proceeds as follows: Section 2 introduces the proposed hybrid model, Section 3 presents the experimental results and discussion, and Section 4 draws the conclusions
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