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Waynelyu/
windSpeedForecast

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Nelson Wind Speed Forecast Project

Environment

Python 3.12.9
GPU: RTX 4080
Memory: 16GB
OS: Linux
Packages: See requirements.txt

Data Sources

Raw data hosted on Google Drive
Automatically downloaded when preprocessing script runs

Project Files

1. data_preprocessing.py

Normalizes data
Creates sliding windows
Generates datasets for training

Run:

python data_preprocessing.py

Outputs:

fold_datasets.pkl: For training
test_dataset.pkl: For testing

Configuration:

batch_size: Smaller = less GPU memory but slower processing
window_shift: Smaller = more data, higher similarity

2. model_builder.py

This file defines the Transformer model architecture. No modifications needed.

Functions:

Transformer layers implementation
Model creation, saving and loading

3. optuna_trial.py

Hyperparameter optimization using Optuna.

Run this file 5 times sequentially (20 epochs per run)
Prevents OOM by splitting 100 epochs into smaller batches

Run:

python optuna_trial.py

4. optuna_analysis.py

Analyzes the hyperparameter search results.

Run:

python optuna_analysis.py

5. training.py

Formal training with best hyperparameters.

Configuration:

epochs: 2000
optimizer_patience: 10 - Switch optimizer after 10 epochs without improvement
early_stop_patience: 25 - Stop training if no improvement for 25 epochs
batch_size: 64 Bigger size have better performance but also require higher GPU memory

Hyperparameters:

python

'model': {
    # Best hyperparameters from analysis
    'd_model': 320,  # Using top performer from group analysis
    'num_heads': 16,  # Using top performer from group analysis
    'dff': 768,  # Using top performer from group analysis
    'num_layers': 2,  # Using top performer from group analysis
    'dropout_rate': 0.0521  # From best trial
}

Manually modify these values if needed.

Run:

python training.py

6. forecast.py

Evaluates model performance and generates forecasts.

Features:

Loads the trained model
Calculates wind power from wind speed predictions
Generates comparison plots for actual vs predicted values
Creates detailed metrics reports

Run:

python forecast.py

Outputs:

Wind speed and power comparison plots
Detailed metrics in forecast_metrics.txt

7. data_analysis folder

Evaluates features relationship, correlation and importance.

Waynelyu/windSpeedForecast

Nelson Wind Speed Forecast Project

Environment

Data Sources

Project Files

1. data_preprocessing.py

2. model_builder.py

3. optuna_trial.py

4. optuna_analysis.py

5. training.py

6. forecast.py

7. data_analysis folder

Waynelyu/
windSpeedForecast