Seasonality in synthetic average wind speed

Miroslav Zivanovic and Mark C. Runacres

Renewable Energy, 2024, vol. 233, issue C

Abstract: There is a growing demand for computer-generated realistic high-fidelity wind speed data for various applications in the wind industry. Such data should capture the non-stationary dynamics of real-world wind time series, as well as be consistent with the statistical descriptors – the probability density function and power spectral density – of the observed wind speed. However, complying with the statistical descriptors is not a guarantee that the seasonality will be correctly reproduced in synthetic data. The seasonality, characterized by the average diurnal and seasonal variations, is driven by the periodicities embedded in diurnal and annual harmonic series respectively. Those periodicities are determined by the long-term orbital forcing components, which establish the insolation for a given latitude and longitude. We show that average diurnal and seasonal variations can be visualized as the output of comb filters, whose fundamental frequencies match the diurnal and annual fundamental frequency respectively. The aforementioned theoretical findings are readily reproduced in synthetic wind speed, generated by a non-parametric data-driven statistical model, based on the phase-randomized Fourier transform. The model, tested on both 10-min and 1-min resolution real-world datasets, yields average non-stationarities in synthetic wind speed with the accuracy close to the computing precision.

Keywords: Wind speed modelling; Seasonality; Comb filtering; Orbital forcing; High-fidelity synthetic wind (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:233:y:2024:i:c:s0960148124012357

DOI: 10.1016/j.renene.2024.121167

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