Wind power is influenced by meteorological conditions such as wind speed,and fluctuates significantly in a short time.Accurate forecasting of wind power can reduce the impact of wind power volatility on the power grid and assist power planning department in formulating better power dispatch plan,ensuring reliability and stability of the power system.To address this,a short-term wind power forecasting method combining wavelet decomposition and neural network was proposed,leveraging the advantages of both signal processing and machine learning.The wind power is treated as signal,the input is transformed into feature through wavelet decomposition,and the feature is extracted by using neural network to produce accurate forecasting.The effectiveness of the proposed method was verified through experiment,and the forecasting accuracy of the proposed method is higher than simple feedforward neural network,recurrent neural network,and long short-term memory neural network.The effective integration of wavelet decomposition and neural network can improve the forecasting accuracy of short-term wind power.
Wavelet DecompositionNeural NetworkWind Power GenerationPowerForecasting
NETL
NSTL
万方数据
