A Critical Review of Wind Power Forecasting Methods—Past, Present and Future

• Published in: Energies (Basel) Vol. 13; no. 15; p. 3764
• Main Authors: Hanifi, Shahram, Liu, Xiaolei, Lin, Zi, Lotfian, Saeid
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2020
• Subjects: Accuracy; Alternative energy sources; artificial neural networks; Computer applications; Computing time; Energy industry; Error analysis; Forecasting; hybrid methods; Identification methods; Maintenance management; Neural networks; Performance evaluation; Prediction models; Renewable resources; Reviews; Statistical methods; System effectiveness; Time series; Turbines; Unit commitment; Weather forecasting; Wind farms; Wind power; wind power forecasting; Wind speed; United Kingdom > UK
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13153764

The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc. More specifically, further recommendations for the research community of wind power forecasting were proposed based on reviewed literature.