## Appendix A  
Devised dual-step integrated ML Algorithm A1 for WPP. ```
1. Begin
2. Enter historical dataset (raw data)
2.1. Historical wind power data (from wind farm SCADA)
2.2. Historical weather data (from WRF model)
3. Apply data treatment
3.1. Change all data to hourly resolution via averaging
3.2. Synchronize the time zones of all the data sources (use CST for all data entry timestamps)
3.3. Fill skipped data points, if any
4. Apply the WT decomposition to the historical target dataset (wind power)
5. Identify training datasets
5.1. For stage #1: Training dataset #1: historical weather variables vs. actual wind speed
5.2. For stage #2: Training dataset #2: historical actual wind speed vs. power
6. Initialize two FFANN networks 6.1. FFANN #1, for stage #1
6.2. FFANN #2, for stage #2
7. Set initial parameters of weights & biases for FFANN #1
8. Train FFANN #1 with Training dataset #1 using the ACO parameter optimization
9. Check convergence of FFANN #1 training 10.1. Save the trained network if the convergence condition is met (this saved network is called Wind Speed Forecaster) 10.1. ## References

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