By Dr. Matthias Lange, Dr. Ulrich Focken (auth.)
From the reviews:
"The publication was once caused through the dramatically boomed wind power utilisation. … it's a textual content ebook for boundary-layer meteorology, movement simulation, time sequence analyses and modelling of the behaviour of wind farms additionally. So the versions are good defined and their software for wind energy prediction is established. … The ebook is supplied for scientists and engineers in general. The textual content is written very transparent. it really is accomplished via 89 figures and thirteen tables." (K. Schäfer, Meteorologische Zeitschrift, Vol. sixteen (3), 2007)
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Additional info for Physical Approach to Short-Term Wind Power Prediction
9) gives an estimation of the velocity ﬂuctuations u in terms of a yet unknown parameter l, the mixing length, which will be determined shortly. 10); hence, w ≈ −u . 7), this leads to 30 3 Foundations of Physical Prediction Models height U(z+l) z+l u’ > 0, w’ < 0 U(z) z u’ < 0, w’ > 0 z−l U(z−l) wind speed Fig. 2. Schematic illustration of the mixing length concept in terms of a wind proﬁle. Fluctuations u in horizontal velocity occur due to ﬂuctuations w of the vertical velocity of ﬂuid elements over distance l.
Stull , Arya  or Lalas and Ratto . There are several numerical models which simulate these effects based on the equations of motion (Chap. 5). g. Gesima , MM5  or Fitnah , with measured data revealed rather 48 4 Physical Wind Power Prediction Systems 100 90 80 height [m] 70 60 50 40 30 unstable profile neutral profile stable profile 20 10 0 2 4 6 8 10 12 wind speed [m/s] Fig. 5. Wind proﬁles for different types of thermal stratiﬁcation having a wind speed of 5 m/s at 10-m height.
The coupling between different ﬂow layers due to turbulent mixing can vary signiﬁcantly within hours. 3 shows time series of measured wind speeds at 10 m and 80 m height together with the corresponding temperature difference between the two heights indicating thermal stratiﬁcation. It can clearly be seen that in 44 4 Physical Wind Power Prediction Systems Fig. 3. Example time series of wind speeds at 10 m and 80 m. 5 m/s in daytime to 5 m/s during the night. These pronounced variations are due to thermal stratiﬁcation, which is indicated here by the temperature difference (T80 − T10 ) between the two heights the daytime, when the temperature near the ground (10 m) is greater than at 80 m, the difference between the wind speeds is small.