Predicting ramps by integrating different sorts of information
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
a e-mail: firstname.lastname@example.org
Received: 23 April 2015
Revised: 22 February 2016
Published online: 25 May 2016
Although predicting sudden rapid changes of renewable energy outputs is useful for maintaining the stability of power grids with many renewable energy resources, the prediction is difficult so far. Here we list causes for the uncertainty for our prediction, quantify them, and forecast whether such sudden rapid changes are likely to happen or not by integrating their quantifications with a method of machine learning. We test the proposed forecast using a toy model and real datasets of solar irradiance and wind speed.
© EDP Sciences, Springer-Verlag, 2016