.png){kind=icon}
Return
Hybrid wind speed prediction framework using data pre-processing strategy based autoencoder network
.png){kind=icon}
PRE
AI
DOI：10.1016/j.epsr.2022.107821.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Wind speed forecasting is required for better wind energy grid integration. Predicting wind speed becomes difficult due to the stochastic nature of the wind. So, this study implemented a hybrid forecasting framework using the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) based convolutional bidirectional long short-term memory (Bi-LSTM) autoencoder for wind speed forecasting (WSF). ICEEMDAN is used for eliminating residual auxiliary noise of the raw wind speed data. Then, a convolutional network encodes the optimal features from the denoised data and the Bi-LSTM decoder interprets the encoded representation to forecast the wind speed effectively. Existing benchmark approaches fail to provide consistency for the different time horizons, addressing this, the proposed framework is tested using 5-min, 10-min and 30min ahead wind speed data. Two experiments have been performed utilizing data from wind farms in Idalia and Garden city to evaluate the novelty and proposed framework's performance. The experimental results of the proposed framework are compared with seven different state-of-the-art models. The proposed approach is evaluated using various performance metrics and the results of experiments I and II demonstrate that the proposed hybrid method outperformed the state-of-the-art WSF models by 21% and 48%, respectively.
Keywords:
Bidirectional-long short-term memory network (Bi-LSTM)
Convolutional neural network (CNN)
Forecasting
Improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)
Uncertainty
Wind
Journal
IF:
4.2
Papers:
1.2W
Citations:
2.2W
Organization
Cited Papers
Design of a combined system based on two-stage data preprocessing and multi-objective optimization for wind speed prediction.png)
ENERGY
ENERGYIF9.4
PREAI
ShareSaveShareSaveA novel combined model for wind speed prediction - Combination of linear model, shallow neural networks, and deep learning approachesENERGY
ENERGYIF9.4
PREAI
ShareSaveShareSaveShareSaveInverse Burr distribution for extreme wind speed prediction: Genesis, identification and estimation
PREAI
ShareSaveShareSaveComparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed predictionAPPLIED ENERGY
APPLIED ENERGYIF11
PREAI
ShareSave
View more