R. Baïle, J.-F. Muzy, and P. Poggi (France)
Wind speed intermittency, cascade model, short term forecasting, conditional laws
This study presents a statistical model of surface layer wind velocity amplitude relying on the notion of continuous cascades. Inspired by recent empirical findings that suggest the existence of some cascading process in the mesoscale range, we consider that wind speed can be described by a seasonal ARMA model where the noise term is “multifractal” i.e., associated with a random cascade. This model can find applications in short-term forecasting: the obtained results show a systematic improvement of the prediction as compared to reference models like persistence. We also address the problem of estimation of wind speed conditional laws. It leads to wind speed distributed according to “transformed” Rice pdf. Such a prediction can be useful to handle specific risk related problems (e.g., the probability that a given event occurs during the next hour or day or the calibration of some “value at risk”). The reliability of the obtained conditional laws are backtested by the means of “p-p plots”.
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