Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

Cornejo-Bueno, Laura; Cuadra, L.; Jiménez-Fernández, Silvia; Acevedo-Rodríguez, Francisco Javier; Prieto, Luís; Salcedo‐Sanz, Sancho

doi:10.3390/en10111784

Cited by 27 publications

(16 citation statements)

References 82 publications

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“…The estimation of the total power collected from wind turbines in a wind farm depends on several factors such as the location, hub height, and season. Cornejo-Bueno et al (2017) [98] applied different machine learning regression techniques to predict WPREs. Variables from atmospheric reanalysis data were used as predictive inputs for the learning machine.…”

Section: (A)mentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

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Section: (A)mentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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“…The proposed hybrid TSVR, RFR, and CNN methods are compared with SVM, 50 and ANN. 49 It is observed that TSVR and RFR models outperformed SVM and ANN models in terms of RMSE and other performance metrics. Further, in terms of computation speed, TSVR is observed to be the fastest among all models.…”

Section: Comparative Analysismentioning

confidence: 89%

Machine intelligent and deep learning techniques for large training data in short‐term wind speed and ramp event forecasting

Dhiman

Deb

2021

Int Trans Electr Energ Syst

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Fluctuation in wind speed over the period of time can impact operation of a utility grid. Sudden large scale variation in turbine power, often termed as ramp event, can threaten the security of power system. In this work, we predict ramp events through a hybrid method based on discrete wavelet transform (DWT) and learning algorithms such as Twin Support vector regression (TSVR), random forest regression (RFR), and Convolutional neural networks (CNN) for onshore, offshore and hilly sites. Wavelet transform-based signal processing helps extract features from wind speed. Results suggest that SVR based prediction models are the best in forecasting among the available models. Besides, CNN predicts ramp events closer to the TSVR model and gives a better prediction performance for larger training datasets. The proposed hybrid version of TSVR, RFR, and CNN models are compared with existing SVM, ANN, and ELM models and indicate significant improvement in predicting ramp events. Compared to SVM, TSVR and RFR are 17.88% and 4.87% efficient in terms of RMSE, respectively. Further, randomness in ramp event signal for all the wind farm sites considered is evaluated using log-energy entropy. Results reveal that compared to wavelet transform, empirical mode decomposition yields lower randomness in predicted ramp event signals. K E Y W O R D Sempirical mode decomposition (EMD), ramp events, support vector regression (SVR), twin support vector regression (TSVR), wavelet transform (WT), wind forecasting | INTRODUCTIONEnvironmental deterioration and greenhouse gas emission is a cause of concern for many developing and developed countries. [1][2][3][4] Among renewable energy sources, by 2020, wind power is likely to cater 12% of global electricity

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“…5 that these two variables have a similar development trend qualitatively, which illustrates that the prediction errors contain the fluctuant elements related to wind power. To quantitatively calculate this correlation, formula (5) is utilised ρ e, P = 0.6037 (13) Result in (13) shows the correlation coefficient is inclined to 1, which illustrates that the prediction errors have a strong correlation with wind power, therefore we could further predict these system errors for correcting the original wind power prediction as (6) and (7).…”

Section: Wind Power Prediction By Model IImentioning

confidence: 99%

“…A probabilistic model using autoregressive logic model was proposed for ramp prediction [12]. Moreover, several machine learning regression models were applied for ramp prediction in literature [13]. While, inspection from the basic structure of event detection models, it is easy to see two directions require to be paid attentions on when studying for improving the performance of ramp prediction, such as preferable ramp detection algorithms and suitable wind power prediction models.…”

Section: Introductionmentioning

confidence: 99%

Ramp events forecasting based on long‐term wind power prediction and correction

Ouyang

Huang

2019

IET Renewable Power Generation

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To mitigate the threat to power system caused by ramp events-large wind power fluctuation, this study proposes an advanced ramp prediction approach based on event detection framework. This approach contains two successive stages of work, including wind power forecasting and ramp detection. Considering high-performance ramp prediction requires long-term and accurate wind power prediction results; this study also proposes a hybrid prediction model at the first stage. By using wind power curve to reflect the physic mechanism of wind power generation, data from numerical weather prediction system could be used to realise long-term trend prediction. Then, a multivariate model is built with a data-mining algorithm to correct system errors of the primary prediction, which is addressed to improve long-term prediction performance. At the second stage, a modified swinging door algorithm is applied for ramp detection. Performance of both the proposed long-term wind power prediction and the corresponding ramp prediction are computed and compared with conventional models on an actual wind dataset. Comprehensive results validated the feasibility and superiority of the proposed ramp prediction approach.

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Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

Cited by 27 publications

References 82 publications

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Machine intelligent and deep learning techniques for large training data in short‐term wind speed and ramp event forecasting

Ramp events forecasting based on long‐term wind power prediction and correction

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