The Autocorrelation of Hourly Wind Speed Observations

Brett, Arthur C.; Tuller, Stanton E.

doi:10.1175/1520-0450(1991)030<0823:taohws>2.0.co;2

Cited by 69 publications

(30 citation statements)

References 11 publications

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“…Their analysis shows that the deterministic component has 1-year, 24-h and 12-h periods. These cycles have also been observed by other authors in surface wind studies (Brett and Tuller, 1991;Gavaldá et al, 1992). The 1-year and 24-h periods are the natural Earth cycles.…”

Section: R C Sreelekshmi Et Al: Deterministic Nature Of Surface Wisupporting

confidence: 84%

Deterministic nature of the underlying dynamics of surface wind fluctuations

Sreelekshmi

Asokan²,

Kumar

2012

Ann. Geophys.

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Abstract. Modelling the fluctuations of the Earth's surface wind has a significant role in understanding the dynamics of atmosphere besides its impact on various fields ranging from agriculture to structural engineering. Most of the studies on the modelling and prediction of wind speed and power reported in the literature are based on statistical methods or the probabilistic distribution of the wind speed data. In this paper we investigate the suitability of a deterministic model to represent the wind speed fluctuations by employing tools of nonlinear dynamics. We have carried out a detailed nonlinear time series analysis of the daily mean wind speed data measured at Thiruvananthapuram (8.483 • N,76.950 • E) from 2000 to 2010. The results of the analysis strongly suggest that the underlying dynamics is deterministic, low-dimensional and chaotic suggesting the possibility of accurate short-term prediction. As most of the chaotic systems are confined to laboratories, this is another example of a naturally occurring time series showing chaotic behaviour.

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Section: R C Sreelekshmi Et Al: Deterministic Nature Of Surface Wisupporting

confidence: 84%

Deterministic nature of the underlying dynamics of surface wind fluctuations

Sreelekshmi

Asokan²,

Kumar

2012

Ann. Geophys.

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“…It is representative of well-exposed coastal stations that have low-friction surroundings and high mean wind speeds. More station details are given in Tuller and Brett (1984;Brett and Tuller, 1991).…”

Section: Stations and Methodsmentioning

confidence: 99%

Measured wind speed trends on the west coast of Canada

Tuller

2004

Intl Journal of Climatology

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106

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Trends in measured wind speed are discussed for four stations on the west coast of Canada. Periods of record vary with the station. They begin in the late 1940s or the 1950s and run through to the early to mid 1990s. The most prominent feature of the time series was a decline in mean annual and winter wind speeds at Cape St James, Victoria International Airport, and Vancouver International Airport during the middle portion of the record. Declines in mean annual wind speed are matched by increases in the percentage of calms and decreases in high wind speed observations. The pressure gradient between Victoria, Vancouver and Comox, the Pacific North American index, the Pacific decadal oscillation index, and other climate elements in British Columbia and the northwestern USA show trends at roughly the same time, indicating a natural cause of the wind speed decrease. Comox Airport mean wind speeds increased, however, perhaps the result of reduced friction in the vicinity of the anemometer outweighing the decrease in the regional pressure gradient.

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“…However the majority of the studies utilize variations of the general ARMA model, (Ailliot et al 2006;Billinton et al 1996;Brett and Tuller 1991;Daniel and Chen 1991;Huang and Chalabi 1995;Kamal and Jafri 1997;Lei et al 2009;Nfaoui et al 1996;Rehman and Halawani 1994;Torres et al 2005). In this paper we will first estimate the dynamics of the detrended and deseasonalized DAWSs process using a general ARMA model and then we will compare our results with a WN.…”

Section: The Linear Arma Modelmentioning

confidence: 99%

Wind Derivatives: Modeling and Pricing

Alexandridis

Zapranis

2012

Comput Econ

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Abstract.Wind is considered to be a free, renewable and environmentally friendly source of energy. However, wind farms are exposed to excessive weather risk since the power production depends on the wind speed and the wind direction. This risk can be successfully hedged using a financial instrument called weather derivatives. In this study the dynamics of the wind generating process are modeled using a non-parametric non-linear wavelet network. Our model is validated in New York. The proposed methodology is compared against alternative methods, proposed in prior studies. We find that wavelet networks can model the wind process very well and consequently they constitute an accurate and efficient tool for wind derivatives pricing. Finally, we provide the pricing equations for wind futures.

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The Autocorrelation of Hourly Wind Speed Observations

Cited by 69 publications

References 11 publications

Deterministic nature of the underlying dynamics of surface wind fluctuations

Deterministic nature of the underlying dynamics of surface wind fluctuations

Measured wind speed trends on the west coast of Canada

Wind Derivatives: Modeling and Pricing

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