Abstract
Wind power generation increases very rapidly in the past few years. The available wind energy is random due to the intermittency and variability of the wind speed. This poses difficulty in the energy dispatched and cause costs, as the wind energy is not accurately scheduled in advance. This paper presents a short-term wind speed forecasting that uses a Kalman filter approach to predict the power production of wind farms. The prediction uses wind speed values measured over a year in a site, on the case study of Portugal. A method to group wind speeds by their similarity in clusters is developed together with a Kalman filter model that uses each cluster as an input to perform the wind power forecasting.
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Ribeiro, F., Salgado, P., Barreira, J. (2012). Wind Power Forecasting to Minimize the Effects of Overproduction. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_26
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DOI: https://doi.org/10.1007/978-3-642-32909-8_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32908-1
Online ISBN: 978-3-642-32909-8
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