Using temperature and state transitions to forecast wind speed

A major issue in forecasting wind speed is non-linear variability. The probability distribution of wind speed series shows heavy tails, while there are frequent state transitions, in which wind speed changes by large magnitudes, over relatively short time periods. These so-called large ramp events are one of the critical issues currently facing the wind energy community. Two forecasting algorithms are analyzed here. The first is a regression on lags, including temperature as a causal factor, with time-varying parameters. The second augments the first using state transition terms. The main innovation in state transition models is that the cumulative density function from regressions on the states is used as a right-hand side variable in the regressions for wind speed. These two methods are tested against a persistence forecast and several non-linear models, using eight hourly wind speed series. On average, these two models produce the best results. The state transition model improves slightly over the regression. However, the improvement achieved by both models relative to the persistence forecast is fairly small. These results argue that there are limits to the accuracy that can be achieved in forecasting wind speed data. Copyright © 2007 John Wiley & Sons, Ltd.

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