Machine Learning Power Generation Prediction
This tool uses machine learning to predict solar power generation based on weather conditions and system parameters. The model has been trained on historical data to provide accurate predictions for various scenarios.
Model Performance Metrics
- Mean Squared Error (MSE): 0.2337
- Root Mean Squared Error (RMSE): 0.4834
- Mean Absolute Error (MAE): 0.2149
- R-squared: 0.8099
Sample Predictions
Below are sample predictions from the model for different input scenarios:
| Scenario | Input Parameters | Predicted Power (kW) |
|---|---|---|
| Scenario 1 |
|
1.94 kW |
| Scenario 2 |
|
0.0 kW |
| Scenario 3 |
|
1.47 kW |
| Scenario 4 |
|
0.04 kW |
| Scenario 5 |
|
0.0 kW |
Apply to Your Project
Select one of your solar projects to analyze with the ML model:
Prediction Result
Estimated power generation: 0.0 kW
Annual estimated production: 0.0 MWh