Wind Turbine Prediction using Deep Learning and Long Short Term Memory (LSTM)

Abstract

Accurate forecasting is essential for the long-term success of adding wind energy to the national power system. In this study, we look at forecasting wind turbine using a LSTM deep learning model. To forecast potential outcomes for a time series, it is sufficient to initially obtain pertinent details from past data. While many methods struggle with understanding the long-term dependencies encoded in data sets, LSTM options, an instance of the strategy in deep learning, show potential for efficiently overcoming this challenge. An overview of LSTM's architecture and forward propagation method is provided initially. LSTM network is applied to the wind turbine prediction dataset. This dataset has 9 features and 6575 records. There are four performance matrices used to test the model. The four matrices are mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). MAPE obtained the least error.