Modeling the fluctuations of wind speed data by considering their mean and volatility effects

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14 Citations (Scopus)

Abstract

An accurate modeling of the fluctuations of wind speed data can always provide a beneficial effect, particularly in regard to wind energy conversion systems. Regarding this matter, a statistical modeling process and analysis has been widely used in the process of wind energy assessment to provide better insight into the behaviors and the variability of the wind regime in a particular area.In fact, a good statistical model will provide accurate forecasting of the wind speed. This will minimize scheduling errors and increase the reliability of the electric power grid. This study investigated the effect of the mean and volatility on the realizations of the wind speed by using a combination of the Autoregressive Integrated Moving Average model and the Autoregressive Conditional Heteroskedasticity model (ARIMA-ARCH model). The results that were obtained show that the ARIMA-ARCH model is able to better forecast the wind speed data than is a single ARIMA model. Thus, it can be conclude that the ARIMA-ARCH model is a good model to use when describing the characteristics of wind speed data.

Original languageEnglish
Pages (from-to)777-784
Number of pages8
JournalRenewable and Sustainable Energy Reviews
Volume54
DOIs
Publication statusPublished - 1 Feb 2016

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Wind power
Energy conversion
Scheduling
Statistical Models

Keywords

  • ARIMA-ARCH model
  • Wind energy
  • Wind speed modeling

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

Cite this

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title = "Modeling the fluctuations of wind speed data by considering their mean and volatility effects",
abstract = "An accurate modeling of the fluctuations of wind speed data can always provide a beneficial effect, particularly in regard to wind energy conversion systems. Regarding this matter, a statistical modeling process and analysis has been widely used in the process of wind energy assessment to provide better insight into the behaviors and the variability of the wind regime in a particular area.In fact, a good statistical model will provide accurate forecasting of the wind speed. This will minimize scheduling errors and increase the reliability of the electric power grid. This study investigated the effect of the mean and volatility on the realizations of the wind speed by using a combination of the Autoregressive Integrated Moving Average model and the Autoregressive Conditional Heteroskedasticity model (ARIMA-ARCH model). The results that were obtained show that the ARIMA-ARCH model is able to better forecast the wind speed data than is a single ARIMA model. Thus, it can be conclude that the ARIMA-ARCH model is a good model to use when describing the characteristics of wind speed data.",
keywords = "ARIMA-ARCH model, Wind energy, Wind speed modeling",
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AB - An accurate modeling of the fluctuations of wind speed data can always provide a beneficial effect, particularly in regard to wind energy conversion systems. Regarding this matter, a statistical modeling process and analysis has been widely used in the process of wind energy assessment to provide better insight into the behaviors and the variability of the wind regime in a particular area.In fact, a good statistical model will provide accurate forecasting of the wind speed. This will minimize scheduling errors and increase the reliability of the electric power grid. This study investigated the effect of the mean and volatility on the realizations of the wind speed by using a combination of the Autoregressive Integrated Moving Average model and the Autoregressive Conditional Heteroskedasticity model (ARIMA-ARCH model). The results that were obtained show that the ARIMA-ARCH model is able to better forecast the wind speed data than is a single ARIMA model. Thus, it can be conclude that the ARIMA-ARCH model is a good model to use when describing the characteristics of wind speed data.

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