Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks (LSTM)

TL;DR

This paper demonstrates that LSTM networks significantly improve wind speed prediction accuracy, aiding wind farm planning and feasibility studies by effectively modeling meteorological data.

Contribution

The study introduces the use of LSTM networks for wind speed prediction and compares its performance with twelve other AI algorithms, highlighting its superior accuracy.

Findings

LSTM achieved 97.8% prediction accuracy.

LSTM outperformed twelve other AI models.

Deep learning enhances wind speed forecasting.

Abstract

Climate change is one of the most concerning issues of this century. Emission from electric power generation is a crucial factor that drives the concern to the next level. Renewable energy sources are widespread and available globally, however, one of the major challenges is to understand their characteristics in a more informative way. This paper proposes the prediction of wind speed that simplifies wind farm planning and feasibility study. Twelve artificial intelligence algorithms were used for wind speed prediction from collected meteorological parameters. The model performances were compared to determine the wind speed prediction accuracy. The results show a deep learning approach, long short-term memory (LSTM) outperforms other models with the highest accuracy of 97.8%.