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Wind forecasts for wind power generation using the Eta model

Wind forecasts for wind power generation using the Eta model,10.1016/j.renene.2009.10.028,Renewable Energy,Lazar Lazić,Goran Pejanović,Momčilo Živkovi

Wind forecasts for wind power generation using the Eta model   (Citations: 2)
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The goal of this article is to apply the regional atmospheric numerical weather prediction Eta model and describe its performance in validation of the wind forecasts for wind power plants. Wind power generation depends on wind speed. Wind speed is converted into power through characteristic curve of a wind turbine. The forecasting of wind speed and wind power has the same principle.Two sets of Eta model forecasts are made: one with a coarse resolution of 22km, and another with a nested grid of 3.5km, centered on the Nasudden power plants, (18.22°E, 57.07°N; 3m) at island Gotland, Sweden. The coarse resolution forecasts were used for the boundary conditions of the nested runs. Verification is made for the nested grid model, for summers of 1996–1999, with a total number of 19 536 pairs of forecast and observed winds. The Eta model is compared against the wind observed at the nearest surface station and against the wind turbine tower 10m wind. As a separate effort, the Eta model wind is compared against the wind from tower observations at a number of levels (38, 54, 75 and 96m).Four common measures of accuracy relative to observations - mean difference (bias), mean absolute difference, root mean square difference and correlation coefficient are evaluated. In addition, scatter plots of the observed and predicted pairs at 10 and 96m are generated. Average overall results of the Eta model 10m wind fits to tower observations are: mean difference (bias) of 0.48m/s, mean absolute difference of 1.14m/s, root mean square difference of 1.38m/s, and the correlation coefficient of 0.79. Average values for the upper tower observation levels are the mean difference (bias) of 0.40m/s; mean absolute difference of 1.46m/s; root mean square difference of 1.84m/s and the correlation coefficient of 0.80.
Journal: Renewable Energy , vol. 35, no. 6, pp. 1236-1243, 2010
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    • ...Goal of [40] is to verify performance of a regional NWP, known as Eta model...
    • ...In hybrid NWPs, forecasting results from NWPs are used as preliminary results and they are fed as input along with generation data from utility to NN or fuzzy structures or other hybrid structures like ANFIS or their combination with other statistical time-series methods like ARMA models, which provide enough downscaling and very good forecasts for a specific wind farm [20], [21], [40], [46]...

    Saurabh S. Somanet al. A review of wind power and wind speed forecasting methods with differe...

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