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The time series power output model of the WPGS was established using (7).
However, the impact of IL and SL for changing the distribution of the time series power demand is required to be reasonably considered in microgrid planning.
Synthetic time series, chaotic Mackey Glass time series, power demand, daily temperatures, stock index, and wind speed are used in a series of experiments.
The temporal relationship between the two time series power signals was explicitly examined using cross-correlation analysis.
For example, the Φ t,40) time series power spectrum had only 1.7% of the temporal variance contributed by harmonics higher than the annual harmonic, whereas the raw Global-SST ENSO index had 9% of the temporal variance contributed by the same harmonics.
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Ulteriorly, the volatility of wind power time series might be over-rated, on condition that the outlier shocks are treated the same as ordinary shocks, and this error evaluation of volatility may harm the performance of wind power forecasting.
Since power calculation is difficult in time series, no power was calculated.
We used Morlet wavelets in Matlab [ 49] to compute the time series' wavelet power spectrum and to identify significant periods for each variable, cross-wavelet power to identify periods where dengue-rainfall and dengue-temperature had high common power, and the coherence spectra to identify local co-variability of dengue-rainfall and dengue-temperature [ 50].
As the transformed frequency within the power spectrum is proportional to the square of the amplitude of this frequency component in the original time series, the power spectrum obtained by FFT was calculated and averaged across the frequency range 0.009 0.08 Hz at each voxel over the time courses.
Fig. 1 Time series and power spectra.
Time series of power demand in MW.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com