New ridge regression, artificial neural networks and support vector machine for wind speed prediction

Zheng, Yufeng; Ge, Yisu; Muhsen, Sami; Wang, Shifeng; Elkamchouchi, Dalia H.; Ali, Elimam; Ali, H. Elhosiny

doi:10.1016/j.advengsoft.2023.103426

Cited by 43 publications

(10 citation statements)

References 58 publications

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“…By incorporating this L2 regularization, ridge regression encourages simpler coefficient values, which, in turn, enhances the algorithm's ability to generalize to new data. The objective function for ridge regression is represented as follows [57,58]:…”

Section: Linear Huber Lasso and Elastic Net Regressions (Enr)mentioning

confidence: 99%

A comprehensive machine learning-based investigation for the index-value prediction of 2G HTS coated conductor tapes

Bonab,

Russo,

Morandi

et al. 2024

Mach. Learn.: Sci. Technol.

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Index-value, or so-called n-value prediction is of paramount importance for understanding the superconductors’ behaviour specially when modelling of superconductors is needed. This parameter is dependent on several physical quantities including temperature, the magnetic field’s density and orientation, and affects the behaviour of HTS devices made out of coated conductors in terms of losses and quench propagation. In this paper, a comprehensive analysis of many machine learning methods for estimating the n-value has been carried out. The results demonstrated that Cascade Forward Neural Network (CFNN) excels in this scope. Despite needing considerably higher training time when compared to the other attempted models, it performs at the highest accuracy, with 0.48 Root Mean Squared Error (RMSE) and 99.72% Pearson coefficient for goodness of fit (R-squared). On the other hand, the Rigid Regression method had the worst predictions with 4.92 RMSE and 37.29% R-squared. Also, Random Forest, boosting methods, and simple Feed Forward Neural Network can be considered as a middle accuracy model with faster training time than CFNN. The findings of this study not only advance modelling of superconductors but also pave the way for applications and further research on machine learning plug-and-play codes for superconducting studies including modelling of superconducting devices.

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Section: Linear Huber Lasso and Elastic Net Regressions (Enr)mentioning

confidence: 99%

A comprehensive machine learning-based investigation for the index-value prediction of 2G HTS coated conductor tapes

Bonab,

Russo,

Morandi

et al. 2024

Mach. Learn.: Sci. Technol.

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“…With the development of artificial intelligence, based on machine learning, forecasting techniques have been divided into shallow and deep learning models. Among them, the shallow learning model includes extreme learning machine (ELM) [9]- [11], back propagation (BP) neural network [12]- [14], and support vector machine (SVM) [15]- [17] Although it can learn nonlinear features adaptively, owing to the limited structure of the shallow learning model, it is prone to problems such as local optimization, poor convergence, and overfitting. Currently, deep learning models are widely applied in wind power forecasting [18]- [22].…”

Section: A Related Workmentioning

confidence: 99%

An Integrated Modeling Strategy for Wind Power Forecasting Based on Dynamic Meteorological Visualization

Zhang,

2024

IEEE Access

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The escalating incorporation of wind energy into power grids imposes constraints on the effective operation and control of power systems. Effective short-term wind power forecasting technology is essential for safe power supply and stable operation of the power grid. Thus, a UMAP-IVMD-ILSTM model for wind power forecasting is proposed based on the uniform manifold approximation and projection (UMAP) integrated improved variational mode decomposition (IVMD) and multi-level improved long and short-term memory (LSTM) networks. First, the UMAP approach was utilized to reduce the dimension of related meteorological indicators, and k-means clustering was applied to classify the weather categories after dimension reduction. Subsequently, through the improved variational mode decomposition, the wind power of the adjacent day is decomposed. Moreover, long and short-term memory networks were optimized using the sparrow search algorithm (SSA) as an improved LSTM (ILSTM), and a wind power forecasting model with multi-level LSTM was established for each mode, and the forecasting results of each mode were integrated. Through actual wind farm operation data, it was proven that the method proposed in this study is effective in processing high-dimensional numerical weather prediction (NWP) data. The model can better preserve the information of the original data and reduce the complexity of the original data sequence based on visualization. Compared with the comparative models, the proposed model reduces the RMSE and MAE by up to 18.2% and 21.6% respectively, and has the least running time, which can effectively improve the accuracy and efficiency of wind power prediction.

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“…In this paper the same kernel functions that were listed in equations ( 2)-( 4) are used for KRR algorithm too. More details on this method can be found on these papers [46][47][48][49][50][51][52].…”

Section: Krrmentioning

confidence: 99%

Estimation of magnetic levitation and lateral forces in MgB₂ superconducting bulks with various dimensional sizes using artificial intelligence techniques

Alipour Bonab,

Xing,

Russo

et al. 2024

Supercond. Sci. Technol.

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The advent of superconducting bulks, because of their compactness and performance, offers new perspectives and opportunities in many applications and sectors, such as magnetic field shielding, motors/generators, NMR/MRI, magnetic bearings, flywheel energy storage, Maglev trains, among others. The investigation and characterization of bulks typically relies on time-consuming and expensive experimental campaigns; hence the development of effective surrogate models would considerably speed up the research progress around them. In this study, we have first produced an experimental dataset with the levitation and lateral forces between different MgB2 bulks and one permanent magnet under different operating conditions. Next, we have exploited the dataset to develop surrogate models based on Artificial Intelligence (AI) techniques, namely Extremely Gradient Boosting (XGBoost), Support Vector Machine Regressor (SVR), and Kernel Ridge Regression (KRR). After the tuning of the hyperparameters of the AI models, the results demonstrated that SVR is the superior technique and can predict levitation and lateral forces with a worst-case accuracy scenario 99.86% in terms of goodness of fit to experimental data. Moreover, the response time of these models for prediction of new datapoints is ultra-fast.

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New ridge regression, artificial neural networks and support vector machine for wind speed prediction

Cited by 43 publications

References 58 publications

A comprehensive machine learning-based investigation for the index-value prediction of 2G HTS coated conductor tapes

A comprehensive machine learning-based investigation for the index-value prediction of 2G HTS coated conductor tapes

An Integrated Modeling Strategy for Wind Power Forecasting Based on Dynamic Meteorological Visualization

Estimation of magnetic levitation and lateral forces in MgB₂ superconducting bulks with various dimensional sizes using artificial intelligence techniques

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New ridge regression, artificial neural networks and support vector machine for wind speed prediction

Cited by 43 publications

References 58 publications

A comprehensive machine learning-based investigation for the index-value prediction of 2G HTS coated conductor tapes

A comprehensive machine learning-based investigation for the index-value prediction of 2G HTS coated conductor tapes

An Integrated Modeling Strategy for Wind Power Forecasting Based on Dynamic Meteorological Visualization

Estimation of magnetic levitation and lateral forces in MgB2 superconducting bulks with various dimensional sizes using artificial intelligence techniques

Contact Info

Product

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Estimation of magnetic levitation and lateral forces in MgB₂ superconducting bulks with various dimensional sizes using artificial intelligence techniques