ARMA based approaches for forecasting the tuple of wind speed and direction

Short-term forecasting of wind speed and direction is of great importance to wind turbine operation and efficient energy harvesting. In this study, the forecasting of wind speed and direction tuple is performed. Four approaches based on autoregressive moving average (ARMA) method are employed for th...

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Bibliographic Details
Published in:	Applied energy Vol. 88; no. 4; pp. 1405 - 1414
Main Authors:	Erdem, Ergin, Shi, Jing
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ltd 01.04.2011 Elsevier
Series:	Applied Energy
Subjects:	Applied sciences ARMA Combined forecasting Combined forecasting Wind speed Wind direction ARMA Vector autoregression Energy energy efficiency Error analysis Exact sciences and technology Forecasting Mathematical analysis Mathematical models Natural energy North Dakota prediction VAR Vector autoregression Vectors (mathematics) Wind direction Wind energy Wind speed Wind turbines North Dakota USA, North Dakota Wind direction Vector autoregression ARMA Wind speed Combined forecasting
ISSN:	0306-2619, 1872-9118
Online Access:	Get full text
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Abstract	Short-term forecasting of wind speed and direction is of great importance to wind turbine operation and efficient energy harvesting. In this study, the forecasting of wind speed and direction tuple is performed. Four approaches based on autoregressive moving average (ARMA) method are employed for this purpose. The first approach features the decomposition of the wind speed into lateral and longitudinal components. Each component is represented by an ARMA model, and the results are combined to obtain the wind direction and speed forecasts. The second approach employs two independent ARMA models – a traditional ARMA model for predicting wind speed and a linked ARMA model for wind direction. The third approach features vector autoregression (VAR) models to forecast the tuple of wind attributes. The fourth approach involves employing a restricted version of the VAR approach to predict the same. By employing these four approaches, the hourly mean wind attributes are forecasted 1-h ahead for two wind observation sites in North Dakota, USA. The results are compared using the mean absolute error (MAE) as a measure for forecasting quality. It is found that the component model is better at predicting the wind direction than the traditional-linked ARMA model, whereas the opposite is observed for wind speed forecasting. Utilizing VAR approaches rather than the univariate counterparts brings modest improvement in wind direction prediction but not in wind speed prediction. Between restricted and unrestricted versions of VAR models, there is little difference in terms of forecasting performance.
AbstractList	Short-term forecasting of wind speed and direction is of great importance to wind turbine operation and efficient energy harvesting. In this study, the forecasting of wind speed and direction tuple is performed. Four approaches based on autoregressive moving average (ARMA) method are employed for this purpose. The first approach features the decomposition of the wind speed into lateral and longitudinal components. Each component is represented by an ARMA model, and the results are combined to obtain the wind direction and speed forecasts. The second approach employs two independent ARMA models - a traditional ARMA model for predicting wind speed and a linked ARMA model for wind direction. The third approach features vector autoregression (VAR) models to forecast the tuple of wind attributes. The fourth approach involves employing a restricted version of the VAR approach to predict the same. By employing these four approaches, the hourly mean wind attributes are forecasted 1-h ahead for two wind observation sites in North Dakota, USA. The results are compared using the mean absolute error (MAE) as a measure for forecasting quality. It is found that the component model is better at predicting the wind direction than the traditional-linked ARMA model, whereas the opposite is observed for wind speed forecasting. Utilizing VAR approaches rather than the univariate counterparts brings modest improvement in wind direction prediction but not in wind speed prediction. Between restricted and unrestricted versions of VAR models, there is little difference in terms of forecasting performance.
Author	Erdem, Ergin Shi, Jing
Author_xml	– sequence: 1 givenname: Ergin surname: Erdem fullname: Erdem, Ergin – sequence: 2 givenname: Jing surname: Shi fullname: Shi, Jing email: jing.shi@ndsu.edu
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10.1016/j.apenergy.2010.10.031_b0040
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Snippet	Short-term forecasting of wind speed and direction is of great importance to wind turbine operation and efficient energy harvesting. In this study, the...
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SubjectTerms	Applied sciences ARMA Combined forecasting Combined forecasting Wind speed Wind direction ARMA Vector autoregression Energy energy efficiency Error analysis Exact sciences and technology Forecasting Mathematical analysis Mathematical models Natural energy North Dakota prediction VAR Vector autoregression Vectors (mathematics) Wind direction Wind energy Wind speed Wind turbines
Title	ARMA based approaches for forecasting the tuple of wind speed and direction
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