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entrix_case_challange/optim_config.yaml
2025-05-06 09:11:36 +02:00

42 lines
2.0 KiB
YAML

# Configuration for the battery optimization runs
# Initial state of charge of the battery (MWh)
initial_b: 0.0
# Maximum energy capacity of the battery (MWh)
max_capacity: 1.0
# Maximum charge/discharge power rate of the battery (MW)
max_rate: 1.0
# The length of the time window (in hours) for which the optimization is run
# This should match the forecast horizon of the models being evaluated.
optimization_horizon_hours: 12
# Output directory for the optimization results
output_dir: 'output/optimization_results'
# List of models to evaluate. Each entry includes the path to the model's
# forecast output file and the path to the forecasting config used for that model.
models:
- name: "LSTM-Single-Model"
type: "model"
# Path to the saved PyTorch model file (.ckpt for type='model') or the ensemble definition JSON file (.json for type='ensemble').
model_path: 'output/classic/best_trial_num90/classic_run/checkpoints/best_classic_model.ckpt'
# Path to the forecasting config (YAML) used for this model training (or for the best trial in an ensemble)
model_config_path: 'output/classic/best_trial_num90/classic_run/config.yaml'
# Path to the target scaler file for the single model (or will be loaded per fold for ensemble).
target_scaler_path: 'output/classic/best_trial_num90/classic_run/classic_target_scaler.pt'
# Path to the data scaler file for the single model (or will be loaded per fold for ensemble).
data_scaler_path: 'output/classic/best_trial_num90/classic_run/classic_data_scaler.pt'
# Path to the input size file for the single model (or will be loaded per fold for ensemble).
input_size_path: 'output/classic/best_trial_num90/classic_run/classic_input_size.pt'
- name: "LSTM-Ensemble"
type: "ensemble"
model_path: "output/ensemble/lstm_price_forecast_best_ensemble.json"
model_config_path: "output/ensemble/lstm_price_forecast_best_config.yaml"
target_scaler_path: None
data_scaler_path: None
input_size_path: None