Load Profiles

Load profiles represent the electricity consumption patterns throughout the year. Enhub provides standard profiles for common household types, and you can also upload custom profiles for more accurate optimization based on real consumption data.

Understanding Load Profiles

A load profile is an 8760-element array (one value per hour for a full year) representing the power consumption pattern in kilowatts (kW) for each hour. When you specify an annual consumption (e.g., 5000 kWh/year), the load profile is scaled to match that total while preserving the consumption pattern.

Standard Profiles

Enhub includes four built-in load profiles optimized for different household types:

Create a custom load profile

curl -X POST https://api.enhub.nl/v1/load-profile \
  -H "Authorization: Bearer {token}" \
  -F "csv=@my_hourly_consumption.csv"

{
  "success": true,
  "record_id": "abc123xyz789",
  "filename": "my_hourly_consumption.csv",
  "data_points": 8760,
  "total_kwh": 4832.45,
  "average_kw": 0.551,
  "hourly_data": [
    0.234,
    0.198,
    0.187,
    0.203,
    "... 8756 more values ..."
  ]
}

{
  "error": "Failed to get uploaded file",
  "message": "csv file is required"
}   

{
  "error": "Failed to create profile record",
  "message": "<REASON OR MESSAGE>"
}   

List custom load profiles

# Get first page (default)
curl -X GET 'https://api.enhub.nl/v1/load-profile' \
  -H "Authorization: Bearer {token}"

# Get specific page with custom page size
curl -X GET 'https://api.enhub.nl/v1/load-profile?page=2&per_page=10' \
  -H "Authorization: Bearer {token}"

{
  "profiles": [
    {
      "record_id": "abc123xyz789",
      "filename": "my_house_2024.csv",
      "total_kwh": 4832.45,
      "average_kw": 0.551,
      "created": "2024-10-14T12:00:00Z",
      "updated": "2024-10-14T12:00:00Z"
    },
    {
      "record_id": "def456uvw012",
      "filename": "summer_consumption.csv",
      "total_kwh": 3245.78,
      "average_kw": 0.370,
      "created": "2024-09-20T08:30:00Z",
      "updated": "2024-09-20T08:30:00Z"
    }
  ],
  "pagination": {
    "page": 1,
    "per_page": 20,
    "total": 2,
    "total_pages": 1,
    "has_next": false,
    "has_prev": false
  }
}

{
  "profiles": [],
  "pagination": {
    "page": 1,
    "per_page": 20,
    "total": 0,
    "total_pages": 0,
    "has_next": false,
    "has_prev": false
  }
}

{
  "error": "Authentication required",
  "message": null
}   

{
  "error": "Failed to fetch profiles",
  "message": "<REASON OR MESSAGE>"
}   

Using Custom Profiles in Scans

Once you've created a custom load profile, use the returned record_id when creating a scan. You can also retrieve the record_id of previously uploaded profiles using the List custom load profiles endpoint.

{
  "goal": "self-sufficiency",
  "yearly_kwh": 5000,
  "load_profile": "abc123xyz789",
  "use_custom_load_profile": true,
  // ... other parameters
}

The yearly_kwh parameter will scale your custom profile to match the specified annual consumption while preserving your consumption pattern. For example, if your uploaded profile totals 4832 kWh but you specify yearly_kwh: 5000, all hourly values will be scaled by a factor of ~1.035 (5000 / 4832).

See the Create a scan documentation for complete details on using custom load profiles in optimization scans.

Best Practices

Preparing Your CSV File

1. Obtain hourly consumption data from your smart meter or energy provider
2. Ensure exactly 8760 rows (one full non-leap year)
3. Use power values (kW), not energy values (kWh per hour)
4. Maintain chronological order starting from January 1st, 12:00 AM
5. Remove headers - the CSV should contain only numeric values
6. Use decimal points (not commas) for decimal values: 0.234 not 0,234

When to Use Custom Profiles

• You have actual smart meter data from your home
• Your consumption pattern differs significantly from standard profiles
• You're analyzing commercial or industrial sites with unique usage patterns
• You want the most accurate optimization results possible

When Standard Profiles Are Sufficient

• You don't have historical consumption data
• You're doing preliminary analysis or comparison
• Your household matches one of the standard profile types
• You're optimizing for a new building without usage history