Getting Started
Learn how to use the Energy Simulations Platform in 5 easy steps
Step 1 Platform Overview
Welcome! Let's start by understanding what this platform can do for you.
Data Management
Upload, organize, and analyze your energy datasets. Support for CSV files with automatic quality checks.
ML Training
Train machine learning models for price and production prediction using Random Forest or Neural Networks.
Predictions
Make predictions on energy prices and solar production using your trained models.
Simulations
Simulate battery storage strategies and optimize for maximum profit with Monte Carlo analysis.
Try Demo Mode
We have pre-loaded sample datasets so you can try everything right away!
Step 2 Upload Your Data
The first step is to get your data into the platform. Here's how:
Supported Formats
CSV files with headers. The platform auto-detects column types (timestamp, numeric, categorical).
Required Columns
For prices: timestamp, price
For production: timestamp, production_kwh
timestamp,price,demand,renewable_share
2024-01-01 00:00,45.23,52100,0.42
2024-01-01 01:00,42.15,48900,0.45
2024-01-01 02:00,38.90,45200,0.48
...
Ready to upload?
Go to "My Data" to upload your first dataset
Step 3 Train Your Models
Once you have data, you can train machine learning models to make predictions.
Random Forest
Robust ensemble method. Great for most use cases. Fast training and good accuracy.
Neural Network
Deep learning approach. Better for complex patterns. Requires more data and tuning.
Training Checklist
- Select your dataset from the dropdown
- Choose prediction type (Price or Production)
- Select the target column to predict
- Configure hyperparameters (or use defaults)
- Click "Train Model" and wait for completion
Ready to train?
Head to the Training page to create your first model
Step 4 Make Predictions
Use your trained models to predict future prices or production values.
Single Prediction
Enter values manually to get a quick prediction for one data point.
Batch Prediction
Upload a CSV file to get predictions for hundreds or thousands of rows at once.
POST /predict/price
{
"model_path": "own/my_price_model.pkl",
"features": {
"demand": 52100,
"renewable_share": 0.42
}
}
Ready to predict?
Go to Predictions to test your models
Step 5 Run Simulations
Simulate battery storage strategies and find the optimal configuration for your needs.
Battery Simulation
Test different battery capacities and charging strategies against real price data.
Monte Carlo Analysis
Run hundreds of scenarios to understand risk and uncertainty in your strategies.
Solar Analysis
Estimate solar production for any location and calculate self-sufficiency rates.
You're Ready!
You've completed the tutorial. Now explore the platform and start optimizing your energy strategy!