Get started with Rebase API in a few simple steps:

1

Create a new site

Explore different site options.

2

Upload generation data

See also how to upload capacity changes.

3

Train ML model

See what training settings are available.

4

Query forecasts

Find out how to query historical forecasts.

​
Step 1. Create a new site

Create a solar site to forecast. You will need to provide some site parameters like location, capacity, etc. More information about the parameters for every type of site are provided in the site set-up guides and in the API Reference.

Create new site
import requests

api_key = "Your API key" # Set your API key

payload = {
	"type": "solar",
	"name": "New solar site",
	"latitude": 52.5,
	"longitude": 13.4,
	"settings": {
		"model_resolution": "1h",
		"usePhysicalModels": True,
		"trainNearTermModels": False,
		"trainLongTermModels": False
	},
	"assets": [
		{
			"azimuth": 180,
			"tilt": 30,
			"capacity": 1000,
			"install_date": "2024-01-01"
		}
	]
}

url = "https://api.rebase.energy/platform/v2/sites"
headers = {"Authorization": api_key, "Content-Type": "application/json"}

response = requests.post(url, json=payload, headers=headers)
response.raise_for_status()
response = response.json()

As response you get the ID of the newly created site.

Response example
{
    "site_id": "a923653c-26f1-1b29-955d-ffde5d182276"
}

​
Step 2. Upload generation data

To train an ML model you need to upload measured power generation data from the site. A good practice for better performance of the ML model is that the data cover a period of one year at least with hourly or more frequent resolution.

Upload data
site_id = "a923653c-26f1-1b29-955d-ffde5d182276" # This is the ID of the new site

payload = {"data": [
        {
            "valid_time": "2024-01-01T12:00:00Z",
            "value": 300.5
        },
		{
            "valid_time": "2024-01-01T13:00:00Z",
            "value": 320.2
        }
    ]
}

url = f"https://api.rebase.energy/platform/v2/sites/{site_id}/actual"

response = requests.post(url, json=payload, headers=headers)
response.raise_for_status()
response = response.json()

When the data have been uploaded successfully, you get a response message.

Response example
{
    "message": "Actuals uploaded successfully"
}

​
Step 3. Train ML model

To start the training of the ML models you will need to provide the site IDs and the training set splits.

Train model
url = "https://api.rebase.energy/platform/v2/sites/models/train"

payload = {
    "site_ids": ["a923653c-26f1-1b29-955d-ffde5d182276"],
    "splits": [
        {
            "end_date": "2023-08-01T00:00Z",
            "start_date": "2023-01-01T00:00Z"
        },
        {
            "end_date": "2024-01-01T00:00Z",
            "start_date": "2023-10-01T00:00Z"
        }
    ]
}

response = requests.post(url, json=payload, headers=headers)
response.raise_for_status()
response = response.json()

The response message informs that the training process has started.

Response example
{
    "message": "Training started"
}

​
Step 4. Query forecast

When training is done, you can query the latest available forecast.

url = f"https://api.rebase.energy/platform/v2/sites/{site_id}/forecast"

response = requests.get(url, headers=headers)
response.raise_for_status()
response = response.json()

The response is a serialized table with the following columns:
valid_time: It contains the target times, i.e. the periods which are forecasted.
forecast: It contains the forecast values.
model_name: It contains the names of the models which create the forecasts.
model_version: It contains the versions of the models which create the forecasts.
update_time: It contains the issue/update time, i.e. the time when the forecasts were updated.

Response example
{
  "valid_time": [
    "2024-01-01T00:00:00Z",
    "2024-01-01T01:00:00Z",
    "2024-01-01T02:00:00Z",
    "2024-01-01T03:00:00Z",
    "2024-01-01T04:00:00Z",
    "2024-01-01T05:00:00Z",
    "2024-01-01T06:00:00Z",
    "2024-01-01T07:00:00Z",
    "2024-01-01T08:00:00Z",
    "2024-01-01T09:00:00Z"
  ],
  "forecast": [
    6000,
    6000,
    6000,
    6000,
    6000,
    6000,
    6000,
    6000,
    6000,
    6000
  ],
  "model_name": [
    "My model",
    "My model",
    "My model",
    "My model",
    "My model",
    "My model",
    "My model",
    "My model",
    "My model",
    "My model"
  ],
  "model_version": [
    1,
    1,
    1,
    1,
    1,
    1,
    1,
    1,
    1,
    1
  ],
  "update_time": [
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z"
  ]
}

Get started with Rebase API in a few simple steps:

1

Create a new site

Explore different site options.

2

Upload generation data

See also how to upload capacity changes.

3

Train ML model

See what training settings are available.

4

Query forecasts

Find out how to query historical forecasts.

​
Step 1. Create a new site

Create a solar site to forecast. You will need to provide some site parameters like location, capacity, etc. More information about the parameters for every type of site are provided in the site set-up guides and in the API Reference.

Create new site
import requests

api_key = "Your API key" # Set your API key

payload = {
	"type": "solar",
	"name": "New solar site",
	"latitude": 52.5,
	"longitude": 13.4,
	"settings": {
		"model_resolution": "1h",
		"usePhysicalModels": True,
		"trainNearTermModels": False,
		"trainLongTermModels": False
	},
	"assets": [
		{
			"azimuth": 180,
			"tilt": 30,
			"capacity": 1000,
			"install_date": "2024-01-01"
		}
	]
}

url = "https://api.rebase.energy/platform/v2/sites"
headers = {"Authorization": api_key, "Content-Type": "application/json"}

response = requests.post(url, json=payload, headers=headers)
response.raise_for_status()
response = response.json()

As response you get the ID of the newly created site.

Response example
{
    "site_id": "a923653c-26f1-1b29-955d-ffde5d182276"
}

​
Step 2. Upload generation data

To train an ML model you need to upload measured power generation data from the site. A good practice for better performance of the ML model is that the data cover a period of one year at least with hourly or more frequent resolution.

Upload data
site_id = "a923653c-26f1-1b29-955d-ffde5d182276" # This is the ID of the new site

payload = {"data": [
        {
            "valid_time": "2024-01-01T12:00:00Z",
            "value": 300.5
        },
		{
            "valid_time": "2024-01-01T13:00:00Z",
            "value": 320.2
        }
    ]
}

url = f"https://api.rebase.energy/platform/v2/sites/{site_id}/actual"

response = requests.post(url, json=payload, headers=headers)
response.raise_for_status()
response = response.json()

When the data have been uploaded successfully, you get a response message.

Response example
{
    "message": "Actuals uploaded successfully"
}

​
Step 3. Train ML model

To start the training of the ML models you will need to provide the site IDs and the training set splits.

Train model
url = "https://api.rebase.energy/platform/v2/sites/models/train"

payload = {
    "site_ids": ["a923653c-26f1-1b29-955d-ffde5d182276"],
    "splits": [
        {
            "end_date": "2023-08-01T00:00Z",
            "start_date": "2023-01-01T00:00Z"
        },
        {
            "end_date": "2024-01-01T00:00Z",
            "start_date": "2023-10-01T00:00Z"
        }
    ]
}

response = requests.post(url, json=payload, headers=headers)
response.raise_for_status()
response = response.json()

The response message informs that the training process has started.

Response example
{
    "message": "Training started"
}

​
Step 4. Query forecast

When training is done, you can query the latest available forecast.

url = f"https://api.rebase.energy/platform/v2/sites/{site_id}/forecast"

response = requests.get(url, headers=headers)
response.raise_for_status()
response = response.json()

The response is a serialized table with the following columns:
valid_time: It contains the target times, i.e. the periods which are forecasted.
forecast: It contains the forecast values.
model_name: It contains the names of the models which create the forecasts.
model_version: It contains the versions of the models which create the forecasts.
update_time: It contains the issue/update time, i.e. the time when the forecasts were updated.

Response example
{
  "valid_time": [
    "2024-01-01T00:00:00Z",
    "2024-01-01T01:00:00Z",
    "2024-01-01T02:00:00Z",
    "2024-01-01T03:00:00Z",
    "2024-01-01T04:00:00Z",
    "2024-01-01T05:00:00Z",
    "2024-01-01T06:00:00Z",
    "2024-01-01T07:00:00Z",
    "2024-01-01T08:00:00Z",
    "2024-01-01T09:00:00Z"
  ],
  "forecast": [
    6000,
    6000,
    6000,
    6000,
    6000,
    6000,
    6000,
    6000,
    6000,
    6000
  ],
  "model_name": [
    "My model",
    "My model",
    "My model",
    "My model",
    "My model",
    "My model",
    "My model",
    "My model",
    "My model",
    "My model"
  ],
  "model_version": [
    1,
    1,
    1,
    1,
    1,
    1,
    1,
    1,
    1,
    1
  ],
  "update_time": [
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z",
    "2024-01-01T00:00:00Z"
  ]
}