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Ambient temperature affects the variation in power demand.
This figure shows the variation in power with height and the tendency to layering.
Generally, wind speed is stochastic, which leads to variation in power coefficient.
Because this tip speed ratio gets affected, it will lead to the variation in power coefficient.
An analysis of Pc1 2 events indicates a seasonal variation, consistent with a similar variation in power.
This technique monitors each variation in power output and integrates those variations over a specified sample period.
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This is done in order to compare the inter-year variation in powers for a given periodicity.
However, the linearly regressed 10.7 cm flux and fractional variation in powers diverge in cycles 22 and 24, indicating that the relationship between the mode powers and the 10.7 cm flux is not consistent from one cycle to the next.
Fig. 3 Daily variations in power.
It shows the variations in power spectral density (PSD) across the radio spectrum from 0 to 6 GHz.
However, questions regarding cost of produced water, variations in power production due to intermittency and environmental effects still remain.
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