Wind Speed Prediction Using a Hybrid Wavelet Analysis and Neural Network Approach

The hybrid methodology combining wavelet analysis and neural networks offers an efficient and precise solution for wind speed prediction. This technique employs wavelet transform to decompose wind speed time series into various frequency components, then utilizes neural networks to model and forecast each component individually, ultimately achieving higher accuracy through signal reconstruction. In implementation, the discrete wavelet transform (DWT) algorithm with appropriate mother wavelets (e.g., Daubechies) is typically applied for decomposition.

In signal processing, wavelet analysis effectively captures non-stationary characteristics of wind speed data by decomposing the original signal into approximation and detail components. This multi-scale decomposition reduces data complexity, making it easier for neural networks to learn underlying patterns. The decomposition process can be implemented using Python's PyWavelets library with functions like wavedec() for multi-level decomposition.

The neural network component commonly employs time series-optimized architectures such as LSTM or GRU networks, trained separately on different frequency components. Since the component signals exhibit greater stationarity and distinct features, neural network training efficiency and prediction accuracy are significantly improved. Implementation typically involves building sequential models with Keras/TensorFlow, using LSTM layers with tanh activation and dropout regularization to prevent overfitting.

The key advantage of this approach lies in combining wavelet analysis' multi-resolution capability with neural networks' nonlinear modeling power, effectively handling uncertainties and sudden changes in wind speed prediction. In practical applications, this method is widely used for wind farm operational management and power grid dispatch optimization, where accurate predictions are crucial for energy scheduling decisions.