Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model

Yousuf, Muhammad Uzair; Al‐Bahadly, Ibrahim; Avci, Ebubekir

doi:10.1109/access.2021.3084536

Cited by 20 publications

(8 citation statements)

References 78 publications

(126 reference statements)

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“…ELM has the advantages of using few training parameters and being simple and easy to use. In addition, ELM has fast learning progress and approximation ability [17]. ELM is used to predict the IMF component of the wind power obtained using VMD in this study.…”

Section: B Extreme Learning Machinementioning

confidence: 99%

Interval Prediction Method for Wind Power Based on VMD-ELM/ARIMA-ADKDE

et al. 2022

IEEE Access

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Section: B Extreme Learning Machinementioning

confidence: 99%

Interval Prediction Method for Wind Power Based on VMD-ELM/ARIMA-ADKDE

et al. 2022

IEEE Access

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“…This paper also uses machine learning methods for time series prediction in the research. In addition, considering that technical means can be used to decompose time series to obtain subsequences [16][17][18]. By modeling each subsequence, good prediction results can be achieved.…”

Section: Introductionmentioning

confidence: 99%

A hybrid MODWT-PSO-SVM model for time series forecasting

Wang

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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Time series usually have certain trends and inherent patterns, so their future values can be predicted based on current information. The time series forecasting with high accuracy can provide decision basis for the relevant personnel, and then improve the efficiency of decision making. There are more and more researches on models for time series forecasting. In view of this, this paper proposes a hybrid MODWT-PSO-SVM model for time series forecasting. It is found that the constructed hybrid model can achieve better predictive performance. Furthermore, this paper investigates the impact of decomposition levels and the form of wavelet functions on the predictive performance of the constructed hybrid model. Research has shown that the predictive performance of the constructed hybrid model tends to decrease first and then increase with the increase of decomposition levels. This means that the predictive performance of the hybrid model varies with the number of decomposition layers, and an optimal number of decomposition layers can be found to achieve the best predictive performance. In addition, different forms of wavelet function also affect the prediction effect of the mixed model. When the Sym4 wavelet is used, the prediction effect of the hybrid model is the best. While when using Coif3 wavelets, the hybrid model has the worst prediction performance. Therefore, the predictive performance of the hybrid model can be improved by adjusting the number of decomposition layers and changing the form of wavelet functions. This research can provide a model reference for time series forecasting and supplement the existing research.

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“…However, previous studies applying only simple models achieved forecasting errors of about 10% [11] for noisy nonlinear data such as wind speed. Therefore, more complex models were proposed for such data, such as data decomposition [5], [12] or considering optimization problems with the cost function being the forecasting error [13]- [15]. This paved the way for the application of deep learning models, which achieved ideal errors of around 5% [15].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Wind Speed Forecasting Accuracy Using a GWO-Nested CEEMDAN-CNN-BiLSTM Model

Phan¹,

Tan²

2023

Preprint

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<p>In the past few years, accurate wind speed forecasting has become progressively important due to the growing demand for renewable energy sources. In this study, we propose a novel artificial model, Grey Wolf Optimization (GWO)-nested Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-Convolutional Neural Network (CNN)-Bidirectional Long Short-Term Memory (BiLSTM), for wind speed forecasting. The proposed model integrates three powerful techniques: CEEMDAN, CNN, and BiLSTM. Firstly, CEEMDAN with two nested layers is used to decompose the wind speed time series into a set of intrinsic mode functions (IMFs) to enhance the accuracy of the model. CNN is then applied to extract features from the IMFs, and BiLSTM is used to capture the temporal dependencies and make accurate predictions. To improve the performance of the model further, we also introduce GWO and apply it to select the best hyperparameters for the forecasting models based on decomposition results. The model is tested using wind speed data collected in Basel in 2022. The final combination result demonstrates that the proposed model achieve a better performance compared to several modern AI-base models in the wind speed forecasting field, with high accuracy reflected by a mean absolute percentage error (MAPE) of nearly 3%.</p>

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Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model

Cited by 20 publications

References 78 publications

Interval Prediction Method for Wind Power Based on VMD-ELM/ARIMA-ADKDE

Interval Prediction Method for Wind Power Based on VMD-ELM/ARIMA-ADKDE

A hybrid MODWT-PSO-SVM model for time series forecasting

Enhancing Wind Speed Forecasting Accuracy Using a GWO-Nested CEEMDAN-CNN-BiLSTM Model

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