Asymmetric Laplace-based wind power forecasting method and system

A method for the asymmetric Laplace-based wind power forecasting, the method comprising:

• Step S1:
• Obtaining the historical wind power data and use MIC to measure linear and nonlinear relationships between random variables to select optimal inputs, whose values of MIC should be greater than the given threshold;
• Step S2:
• Constructing a neural network-based forecasting model with a multi-scale feature fusion module, the input of the model is a 1×1×d×1 tensor, wherein d is the input dimension;
• Step S3:
• Deriving the loss function based on the maximum likelihood estimation of the asymmetric Laplace distribution, the model is trained with the selected inputs and the desired output to predict the parameters of the asymmetric Laplace distribution;
• Step S4:
• Collecting the estimated parameters of the asymmetric Laplace distribution from the trained model, and using the mean statistics of the estimated asymmetric Laplace distribution as the deterministic forecast of wind power.