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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Vydáno v:Applied energy Ročník 88; číslo 4; s. 1405 - 1414
Hlavní autoři: Erdem, Ergin, Shi, Jing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ltd 01.04.2011
Elsevier
Edice:Applied Energy
Témata:
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
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Shrnutí: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.
Bibliografie:http://dx.doi.org/10.1016/j.apenergy.2010.10.031
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2010.10.031