How to get model inputs

What are model inputs

Every forecasting model is driven by input data, mainly weather variables coming from numerical weather prediction models. A single forecasting model can combine several weather sources at the same time, so that it does not depend on one weather provider alone. The Get Model Input endpoint returns the input data of the model’s latest run. It is useful when you want to inspect the weather that a forecast was based on, for example when investigating a large forecast error. To get the output which the model produced from these inputs, see how to get model output.

How to query the model inputs

The only parameter you need to provide is the model ID, which you can retrieve with the List Site Models endpoint, as described in Step 1 of the model output guide. The endpoint takes no further request parameters. It always returns the inputs of the most recent model run, with the target times in UTC.

Get model inputs

import requests

api_key = "Your API key" # Set your API key
model_id = "1d567fa0-e356-467a-cd9f-5f78b901c345" # Set the model ID

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

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

In the response example below the arrays are truncated to five time steps.

Response example

{
    "dataframe": {
        "DWDOM_SolarDownwardRadiation": [
            23.0,
            54.0,
            93.0,
            153.0,
            249.0
        ],
        "ECHRESOM_SolarDownwardRadiation": [
            6.0,
            51.0,
            136.0,
            224.0,
            262.0
        ],
        "GFSOM_SolarDownwardRadiation": [
            10.0,
            105.0,
            223.0,
            314.0,
            424.0
        ],
        "HIRESMEPSOM_SolarDownwardRadiation": [
            32.0,
            58.0,
            97.0,
            156.0,
            211.0
        ],
        "valid_time": [
            "2025-10-01T05:00Z",
            "2025-10-01T06:00Z",
            "2025-10-01T07:00Z",
            "2025-10-01T08:00Z",
            "2025-10-01T09:00Z"
        ]
    },
    "metadata": {
        "DWDOM_SolarDownwardRadiation": {
            "type": "numeric"
        },
        "ECHRESOM_SolarDownwardRadiation": {
            "type": "numeric"
        },
        "GFSOM_SolarDownwardRadiation": {
            "type": "numeric"
        },
        "HIRESMEPSOM_SolarDownwardRadiation": {
            "type": "numeric"
        }
    }
}

The response contains the following fields:
dataframe: A serialized table with one column per input feature, plus a valid_time column which contains the target times, i.e. the periods which are forecasted.
metadata: The data type of every input feature, for example numeric for the weather variables. Each input feature is named after the weather source it comes from and the weather variable it holds, separated by an underscore. In the example above the model is driven by SolarDownwardRadiation from four different weather sources, DWDOM, ECHRESOM, GFSOM and HIRESMEPSOM.

Load the inputs into a DataFrame

The dataframe field is column oriented, so it can be handed straight to pandas.

Load model inputs into a DataFrame

import pandas as pd

df = pd.DataFrame(response["dataframe"])
df["valid_time"] = pd.to_datetime(df["valid_time"])
df = df.set_index("valid_time")

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What are model inputs

How to query the model inputs

Load the inputs into a DataFrame

​What are model inputs

​How to query the model inputs

​Load the inputs into a DataFrame

What are model inputs

How to query the model inputs

Load the inputs into a DataFrame