Current methods and advances in forecasting of wind power generation

By Aoife M. Foley, Paul Leahy, Antonino Marvuglia, Eamon McKeogh. 2012

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

A survey of data fusion in smart city applications (2019, background): “Nowadays, the most common renewable energy sources emerged in the market are solar farm [101], [141] and wind power [215], [216].”
Risk averse optimal operation of a virtual power plant using two stage stochastic programming (2014, background): “Modeling the WPP and PV power plants and the market prices are discussed in [29, 30] and [31].”
Current status and future advances for wind speed and power forecasting (2014, background): “Many different approaches are employed [2, 3, 5, 6, 11, 13].”
Potential applications of subseasonal‐to‐seasonal (S2S) predictions (2017, background): “For instance, it is routine practice for the wind energy sector to use short-range weather forecasts (Barthelmie et al., 2008; Foley et al., 2012) and, to a lesser degree, seasonal outlooks (Roulston et al.”
Markov chain modeling for very-short-term wind power forecasting (2015, background): “Wind power forecasting methods can be divided into two main roups: physical and statistical methods [1,2].”
Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review (2019, background): “Hence proper forecasting is required for wind energy based power grids, and can aid in making operational strategies [139]–[142].”