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Two limited-area atmospheric models have been employed, with different options/capabilities of horizontal resolution, to provide wind speed forecasts.
Thus, two to six-step ahead wind speed forecasts can be used in some practical applications, such as the load dispatch planning and the load increment/decrement decisions.
Accurate multi-step ahead wind speed forecasts make the power system be adjusted timely and properly to ensure the stable and efficient operation of power system.
The computational results show that the suggested hybrid model favorably improves point wind speed forecasts in comparison with other models and provides satisfactory interval wind speed prediction.
Moreover, temporal evolution of surface parameters such as temperature, moisture and wind speed forecasts associated with monsoon is also improved with GHRSST forcing as a lower boundary condition.
The MAEs of their hour-ahead wind speed forecasts are between (0.9,hbox {m}/hbox {s}) and (0.95,hbox {m}/hbox {s}) during the day and are between (1.01,hbox {m}/hbox {s}) and (1.07,hbox {m}/hbox {s}) overnight.
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Many wind speed forecasting algorithms have been proposed to improve prediction accuracy.
However, the correlation among different forecasting steps is often neglected in current multi-step ahead wind speed forecasting approaches, and the characteristic of heteroscedasticity in wind speed forecasting errors is usually not taken into consideration.
Thus, a correlation aware multi-step ahead wind speed forecasting technique with heteroscedastic multi-kernel learning is designed.
In this paper, the experimental results in different wind farms and different seasons prove that the regression model considering the characteristics of multi-step ahead wind speed forecasting, task correlation and heteroscedasticity, will produce more accurate forecasts than the other models as for two to six-ahead wind speed forecasting.
The effectiveness of the proposed forecast strategy to predict wind speed is evaluated by the trials of 10 min ahead wind speed forecasting at two locations of the National Renewable Energy Laboratory NRELL).
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