Short-Horizon Prediction of Wind Power: A Data-Driven Approach

• Published in: IEEE transactions on energy conversion Vol. 25; no. 4; pp. 1112 - 1122
• Main Authors: Kusiak, A, Zijun Zhang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2010; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computation; Data mining; Direct power generation; evolutionary strategy (ES) algorithm; exponential smoothing; Mathematical models; Neural networks; neural networks (NNs); Power generation; power prediction; Power system modeling; Predictive models; Smoothing; Smoothing methods; time-series model; Wind energy; Wind energy generation; Wind forecasting; Wind power; Wind power generation; Wind speed; wind speed prediction; Wind turbines

This paper discusses short-horizon prediction of wind speed and power using wind turbine data collected at 10 s intervals. A time-series model approach to examine wind behavior is studied. Both exponential smoothing and data-driven models are developed for wind prediction. Power prediction models ar...