https://orcid.org/0000-0002-3662-1832
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ABSTRACT
Most forecasting algorithms are tuned to a specific location or dataset and will not perform well in other situations. Some wind speed data might contain outliers, missed values, or noise which affects the forecasting performance tremendously. This paper proposes a hybrid-forecasting model that includes pre-processing and deep learning techniques to bridge the gap in developing a generic forecasting algorithm that is unspecific of location or dataset. The proposed model includes a preprocessing part that consists of missed value and outlier handling, decomposition, Kalman filtering, and smoothing. This is an important step because no matter how accurate the forecasting model is, results will vary tremendously if the dataset is corrupted. In addition to that, three different deep-base learning algorithms RNN, GRU, and LSTM will be used based on the characteristics of each subseries to reduce the complexity of the overall forecasting model. The proposed model performed the best across the seven tested sites from different locations with different climates and geography. Compared to other forecasting models such as LSTM standalone and EWT-LSTM, a performance improvement in accuracy by 50% as well as a 25% reduction in processing time was achieved with the proposed forecasting model
Nomenclature
| ANN | = | Artificial Neural Networks |
| ARMA | = | Auto-Regressive Moving Average |
| ARIMA | = | Auto-Regressive Integrated Moving Average |
| BPNN | = | Back Propagation Neural Network |
| BiLSTM | = | Bi-directional long short-term memory |
| DNN | = | Deep Neural Network |
| ELM | = | Extreme Learning Machine |
| EMD | = | Empirical Modal Distribution |
| EEMD | = | Ensemble Empirical Mode Decomposition |
| EWT | = | Empirical wavelets transform |
| GRU | = | Gated recurrent unit |
| GM | = | Gray Model |
| GRNN | = | Generalized Regression Neural Network |
| IQR | = | Inter-Quartile Range |
| LSTM | = | Long short-term memory |
| MODA | = | Multi-Objective Dragonfly Algorithm |
| MOMVO | = | Multi-Objective Multi-Verse optimizer |
| NWP | = | Numerical Weather Prediction |
| RNN | = | Recurrent neural network (RNN) |
| SVM | = | Support Vector Machine |
| SSA | = | Singular spectrum analysis |
| SSEA | = | Stacked sparse autoencoder |
| WD | = | Wavelet Decomposition |
| WPD | = | Wavelets Packet Decomposition |
Disclosure statement
No potential conflict of interest was reported by the author(s).
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/15435075.2023.2228878