Short term wind speed forecasting using artificial and wavelet neural networks with and without wavelet filtered data based on feature selections technique

Ali, Yousef; Aly, Hamed H.

doi:10.1016/j.engappai.2024.108201

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…The Tidal Current Turbine Model Driven by DDPMSG [3,[29][30][31][32] The tidal current power (P t ) is represented by…”

Section: And the Updating Uses Residual Ymentioning

confidence: 99%

A Proposed Hybrid Machine Learning Model Based on Feature Selection Technique for Tidal Power Forecasting and Its Integration

Aly

2024

Electronics

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Renewable energy resources are playing a crucial role in minimizing fossil fuel emissions. Integrating machine learning techniques with tidal power forecasting could greatly enhance the accuracy and reliability of predictions, which is crucial for efficient energy production and management. A hybrid approach combining different methods often yields better results than relying on individual techniques. The accuracy of tidal current power is very important, especially for smart grid applications. This work proposes hybrid adaptive neuro-fuzzy inference system (ANFIS) with the Kalman filter (KF) and a neuro-wavelet (WNN) for tidal current speed, direction, and power forecasting. The turbine used in this study is driven by a direct drive permanent magnet synchronous generator (DDPMSG). The predictions of individual and hybrid models including the ANFIS, the Kalman filter, and the WNN for tidal current speed and the power it generates are compared with another dataset as a way of validation which is the tidal currents direction. Also, other published work results in the literature are compared to the proposed work. Different hybrid models are proposed for smart grid integration. The results of this work indicate that the hybrid model of the WNN and the ANFIS for tidal current power or speed forecasting has the highest performance compared to all other models.

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“…The Tidal Current Turbine Model Driven by DDPMSG [3,[29][30][31][32] The tidal current power (P t ) is represented by…”

Section: And the Updating Uses Residual Ymentioning

confidence: 99%

A Proposed Hybrid Machine Learning Model Based on Feature Selection Technique for Tidal Power Forecasting and Its Integration

Aly

2024

Electronics

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“…Lanzaco et al integrated an artificial neural network and SVM to obtain an improved AOD map for South America [26]. The deep learning approach performs better in solving nonlinear problems of atmospheric characteristics [27][28][29]. A deep learning model with multiple hidden layers can simulate highly varying functions defining nonlinear structures [30,31], which is suitable for the determination of the nonlinear relationships between satellite measurements and the AOD.…”

Section: Introductionmentioning

confidence: 99%

Wide and Deep Learning Model for Satellite-Based Real-Time Aerosol Retrievals in China

Luo,

Zou,

Zang

et al. 2024

Atmosphere

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Machine learning methods have been recognized as rapid methods for satellite-based aerosol retrievals but have not been widely applied in geostationary satellites. In this study, we developed a wide and deep learning model to retrieve the aerosol optical depth (AOD) using Himawari-8. Compared to traditional deep learning methods, we embedded a “wide” modeling component and tested the proposed model across China using independent training (2016–2018) and test (2019) datasets. The results showed that the “wide” model improves the accuracy and enhances model interpretability. The estimates exhibited better accuracy (R2 = 0.81, root-mean-square errors (RMSEs) = 0.19, and within the estimated error (EE) = 63%) than those of the deep-only models (R2 = 0.78, RMSE = 0.21, within the EE = 58%). In comparison with extreme gradient boosting (XGBoost) and Himawari-8 V2.1 AOD products, there were also significant improvements. In addition to higher accuracy, the interpretability of the proposed model was superior to that of the deep-only model. Compared with other seasons, higher contributions of spring to the AOD concentrations were interpreted. Based on the application of the wide and deep learning model, the near-real-time variation of the AOD over China could be captured with an ultrafine temporal resolution.

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Predictive analysis of stochastic stock pattern utilizing fractional order dynamics and heteroscedastic with a radial neural network framework

Bukhari,

Raja,

Alquhayz

et al. 2024

Engineering Applications of Artificial Intelligence

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Short term wind speed forecasting using artificial and wavelet neural networks with and without wavelet filtered data based on feature selections technique

Cited by 5 publications

References 31 publications

A Proposed Hybrid Machine Learning Model Based on Feature Selection Technique for Tidal Power Forecasting and Its Integration

A Proposed Hybrid Machine Learning Model Based on Feature Selection Technique for Tidal Power Forecasting and Its Integration

Wide and Deep Learning Model for Satellite-Based Real-Time Aerosol Retrievals in China

Predictive analysis of stochastic stock pattern utilizing fractional order dynamics and heteroscedastic with a radial neural network framework

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