Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks (LSTM)
This work addresses the challenge of predicting wind speed for renewable energy planning, but it is incremental as it applies an existing method (LSTM) to a specific dataset.
The paper tackled wind speed prediction to aid wind farm planning by comparing twelve AI algorithms, finding that LSTM achieved the highest accuracy of 97.8%.
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%.