Wind Energy Forecasting with Neural Networks: A Literature Review

Authors

  • Jaume Manero Universitat Politecnica de Catalunya Barcelona TECH, Barcelona
  • Javier Béjar Universitat Politecnica de Catalunya Barcelona TECH, Barcelona
  • Ulises Cortés Barcelona Supercomputing Center, Barcelona

DOI:

https://doi.org/10.13053/cys-22-4-3081

Keywords:

Wind power forecast, wind speed forecast, short-term prediction, machine learning, deep learning, neural networks

Abstract

Renewable energy is intermittent by nature and to integrate this energy into the Grid while assuring safety and stability the accurate forecasting of there newable energy generation is critical. Wind Energy prediction is based on the ability to forecast wind. There are many methods for wind forecasting based on the statistical properties of the wind time series and in the integration of meteorological information, these methods are being used commercially around the world. But one family of new methods for wind power fore castingis surging based on Machine Learning Deep Learning techniques. This paper analyses the characteristics of the Wind Speed time series data and performs a literature review of recently published works of wind power forecasting using Machine Learning approaches (neural and deep learning networks), which have been published in the last few years.

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Published

2018-12-30

Issue

Vol. 22 No. 4 (2018): Topic Trends in Computing Research (Guest Editors: A. Aguilar-Meléndez, E. Moya-Sánchez)

Section

Articles of the Thematic Issue

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