Current status and future advances for wind speed and power forecasting

By Jaesung Jung, Robert P. Broadwater. 2014

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Citations in the corpus

A survey of data fusion in smart city applications (2019, background): “Meanwhile in [217], Jung and Broadwater have implemented a statistical model to fuse wind speed, direction, temperature from forecast station and online measurement to determine the total power output of the wind farm.”
Rough Deep Neural Architecture for Short-Term Wind Speed Forecasting (2017, methodology): “This approach mainly uses previous history data in order to design a statistical model and yields accurate performance for short-term forecasting tasks [7].”
Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting (2019, background): “The Persistence (PR) model [2] is further presented as a benchmark due to its decent performance for ultra-short-term and short-term predictions.”
Wind Power Curve Modeling and Wind Power Forecasting With Inconsistent Data (2019, background): “Therefore, more accurate wind power forecasting models are necessary for integration purpose, which are also helpful to reduce the operating costs [3], [4].”
Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity (2016, background): “The literature on contributed models for wind power and wind speed forecasts is vast, Jung and Broadwater (2014) as well as Tascikaraoglu and Uzunoglu (2014) provide an up-to-date overview.”
A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting (2017, background): “Jung and Broadwater (2014) present a possible definition for short-term (30min - 6h) and medium-term (6h - 24h) wind speed prediction approaches.”