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- W4313143376 abstract "Forecasting wind power for geographically dispersed multiple wind farms is a challenge for system operators. Further, power forecast for a single wind farm leads to considerable forecast error due to the stochastic nature of wind and associated nonlinearity of turbines' power curves. This error can be drastically decreased with the help of aggregated power forecasting of wind farms in a large region utilizing spatial smoothing effects. Forecasting through the hierarchical aggregation of wind farms allows forecasts at different levels of power system networks. Hierarchical aggregated forecasting is widely implemented using either top-down or bottom-up approaches. However, these methods use wind power data patterns using a single level to get the forecasts of the entire hierarchy and thus result in lower prediction accuracy. In this context, the present work proposes combinations of top-down and bottom-up methods using a hierarchical time series framework. Several combinations of top-down and bottom-up forecast approaches are tested and results show that the Variance-Covariance method provides the best results in terms of prediction accuracy." @default.
- W4313143376 created "2023-01-06" @default.
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- W4313143376 date "2022-07-17" @default.
- W4313143376 modified "2023-10-17" @default.
- W4313143376 title "Spatial Hierarchical Wind Power Forecasting" @default.
- W4313143376 doi "https://doi.org/10.1109/pesgm48719.2022.9916908" @default.
- W4313143376 hasPublicationYear "2022" @default.
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