Short-term wind power forecasting through stacked and bi directional LSTM techniques.

ABSTRACT:

Background

Computational intelligence (CI) based prediction models increase the efficient and effective utilization of resources for wind prediction. However, the traditional recurrent neural networks (RNN) are difficult to train on data having long-term temporal dependencies, thus susceptible to an inherent problem of vanishing gradient. This work proposed a method based on an advanced version of RNN known as long short-term memory (LSTM) architecture, which updates recurrent weights to overcome the vanishing gradient problem. This, in turn, improves training performance.

Methods

The RNN model is developed based on stack LSTM and bidirectional LSTM. The parameters like mean absolute error (MAE), standard deviation error (SDE), and root mean squared error (RMSE) are utilized

SUBMITTER: Ali Khan M 

PROVIDER: S-EPMC11042035 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature