A Stochastic Recurrent Encoder Decoder Network for Multistep Probabilistic Wind Power Predictions.

Area of Science:

• Renewable Energy Systems
• Artificial Intelligence
• Statistical Modeling

Background:

• Accurate wind power prediction is crucial for grid stability and energy management.
• Traditional methods struggle with the inherent uncertainty and complex patterns in wind energy generation.

Purpose of the Study:

• To introduce a novel stochastic recurrent encoder decoder neural network (SREDNN) for generative multistep probabilistic wind power predictions (MPWPPs).
• To enhance MPWPPs by integrating exogenous covariates and latent random variables within an encoder-decoder framework.

Main Methods:

• Developed a SREDNN incorporating latent random variables in recurrent structures.
• Utilized an encoder-decoder framework with five components: prior, inference, generative, encoder recurrent, and decoder recurrent networks.
• Integrated latent variables to build an infinite Gaussian mixture model (IGMM) for the observation model and employed stochastic hidden state updates.

Main Results:

• The SREDNN demonstrated superior performance in MPWPP compared to conventional RNN-based methods.
• Achieved a lower negative continuously ranked probability score (CRPS*), indicating improved prediction accuracy.
• Showcased superior sharpness and comparable reliability in prediction intervals.

Conclusions:

• The SREDNN effectively models complex patterns in wind speed and power sequences by leveraging latent random variables.
• The integration of latent random variables significantly enhances the expressiveness of wind power distribution models.
• The developed SREDNN offers a powerful and effective approach for advanced wind power forecasting.