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A hybrid wind power prediction method

As a type of clean and renewable energy source, wind power is being widely used all around the world. However, owing to the uncertainty and instability of the wind power, it is important to build an accurate prediction model for wind power for the grid-connected security operation. The performance of hybrid method is always better than that of single ones in the wind power prediction. Actual wind power can be decomposed into hourly level and turbulence level. Key factors in the wind power hourly level data can be found by Gray Correlation Analysis. The hidden rules of wind power turbulence level is extracted by historical data from wind farm based on deep belief network (DBN). Several experiments are conducted to compare different solutions. The experimental results show that prediction errors are significantly reduced using the proposed technique.