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

1
Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805 Madrid, Spain
2
Iberdrola, 28033 Madrid, Spain
*
Author to whom correspondence should be addressed.
Energies 2017, 10(11), 1784; https://doi.org/10.3390/en10111784
Received: 1 October 2017 / Revised: 29 October 2017 / Accepted: 31 October 2017 / Published: 6 November 2017
Wind Power Ramp Events (WPREs) are large fluctuations of wind power in a short time interval, which lead to strong, undesirable variations in the electric power produced by a wind farm. Its accurate prediction is important in the effort of efficiently integrating wind energy in the electric system, without affecting considerably its stability, robustness and resilience. In this paper, we tackle the problem of predicting WPREs by applying Machine Learning (ML) regression techniques. Our approach consists of using variables from atmospheric reanalysis data as predictive inputs for the learning machine, which opens the possibility of hybridizing numerical-physical weather models with ML techniques for WPREs prediction in real systems. Specifically, we have explored the feasibility of a number of state-of-the-art ML regression techniques, such as support vector regression, artificial neural networks (multi-layer perceptrons and extreme learning machines) and Gaussian processes to solve the problem. Furthermore, the ERA-Interim reanalysis from the European Center for Medium-Range Weather Forecasts is the one used in this paper because of its accuracy and high resolution (in both spatial and temporal domains). Aiming at validating the feasibility of our predicting approach, we have carried out an extensive experimental work using real data from three wind farms in Spain, discussing the performance of the different ML regression tested in this wind power ramp event prediction problem. View Full-Text
Keywords: wind energy; wind power ramp events; machine learning regressors; reanalysis; Gaussian processes; support vector machines; neural networks wind energy; wind power ramp events; machine learning regressors; reanalysis; Gaussian processes; support vector machines; neural networks
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Cornejo-Bueno, L.; Cuadra, L.; Jiménez-Fernández, S.; Acevedo-Rodríguez, J.; Prieto, L.; Salcedo-Sanz, S. Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data. Energies 2017, 10, 1784.

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