1.8 KiB
1.8 KiB
Papers we are basing it off of
We are basing it off of very recent papers in which ML models have been used for higher forecasting power.
- Short-term wind speed prediction using Extended Kalman Filter and ML
- Research on short-term wind speed
- Real-time Forecasting Framework using Deep Learning
- codebase for their paper
- Geophysical Constraints worldwide
- Accelerating Weather Prediction using Near-Memory Reconfigurable Fabric
- Feasibility of soft computing for estimating long-term monthly mean wind speed
- Visual Wind Speed prediction using CNN & RNN
- For Wind Energy res-=ource quantification, air pollution monitoring, and weather forecasting
- Short-term wind speed prediction to correct numerical weather forecasting
- Specifically, the values of the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE) are 0.1042 m/s, 4.63% and 0.1309 m/s after correction, decreased by 94.13%, 91.75% and 93.93%, respectively, compared to those without correction.
Further Reading
- Heaven's Breath: A Natural History of the Wind by Lyall Watson