Wind Speed Prediction and Visualization Using Long Short-Term Memory
Networks (LSTM)
- URL: http://arxiv.org/abs/2005.12401v1
- Date: Fri, 22 May 2020 17:51:13 GMT
- Title: Wind Speed Prediction and Visualization Using Long Short-Term Memory
Networks (LSTM)
- Authors: Md Amimul Ehsan, Amir Shahirinia, Nian Zhang, Timothy Oladunni
- Abstract summary: This paper proposes the prediction of wind speed that simplifies wind farm planning and feasibility study.
The results show a deep learning approach, long short-term memory (LSTM) outperforms other models with the highest accuracy of 97.8%.
- Score: 1.8495489370732452
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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%.
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