Energy Consumption#

This example demonstrates Tabular Regression using the Energy Consumption dataset.

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Problem Description#

Goal: Predict the energy consumption of a house based on environmental factors.

Input Features (5):

  1. Temperature difference (inside vs. outside)

  2. Wind direction

  3. Wind speed

  4. Sunshine intensity

  5. Relative humidity

Output (1):

  • Energy consumption

Dataset#

The dataset is split into training and test sets:

  • x_train.csv — Training inputs (5 features)

  • y_train.csv — Training outputs (energy consumption)

  • x_test.csv — Test inputs

  • y_test.csv — Test outputs (for evaluation)

Note

These files ship in the EnergyConsumption example folder of the NeurEco installation (see Examples for the location). Extract that folder and run the commands below from inside it so the relative paths resolve.

Building the Model#

  1. Launch NeurEco and select Tabular Regression

  2. Load x_train.csv as input and y_train.csv as output

  3. Optionally load test files for automatic evaluation

  4. Click BUILD

  5. Monitor training progress

  6. Once complete, evaluate and export

Expected Results#

After training, you should observe:

  • Small, parsimonious network

  • Quick training — Usually completes within minutes

Key Takeaways#

This example demonstrates:

  1. Data efficiency — NeurEco achieves good results with modest training data

  2. Automatic architecture — No need to manually design the network

  3. Checkpoint availability — Multiple models saved during training

  4. Easy export — One command to export for deployment

Variations to Try#

  • Adjust valid_percentage to see its effect on model selection

  • Try different normalization settings

  • Limit model size with links_maximum_number

  • Compare different checkpoints’ performance on test data