Title :
Short-Horizon Prediction of Wind Power: A Data-Driven Approach
Author :
Kusiak, Andrew ; Zhang, Zijun
Author_Institution :
Intell. Syst. Lab., Univ. of Iowa, Iowa City, IA, USA
Abstract :
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 are established, which are based on the most effective wind prediction model. Comparative analysis of the power predicting models is discussed. Computational results demonstrate performance advantages provided by the data-driven approach. All computations reported in the paper are based on the data collected at a large wind farm.
Keywords :
data mining; neural nets; power engineering computing; prediction theory; statistical analysis; wind power plants; data mining; evolutionary strategy; exponential smoothing; neural networks; short horizon prediction; time-series model; wind power; wind speed prediction; wind turbine data; Neural networks; Power generation; Power system modeling; Predictive models; Smoothing methods; Wind energy; Wind energy generation; Wind forecasting; Wind speed; Wind turbines; Data mining; evolutionary strategy (ES) algorithm; exponential smoothing; neural networks (NNs); power prediction; time-series model; wind speed prediction;
Journal_Title :
Energy Conversion, IEEE Transactions on
DOI :
10.1109/TEC.2010.2043436
