GET /metrics/accuracy — Forecast Accuracy Statistics

The GET /metrics/accuracy endpoint returns error statistics comparing Nexenergie’s past forecasts to the actual prices published by OMIE. Use these metrics to gauge current model performance before making decisions — for example, when sizing risk buffers for energy procurement or validating forecast quality ahead of a new trading period.

Endpoint

Property	Value
Method	`GET`
Path	`/api/v1/metrics/accuracy`
Auth	Required — `Authorization: Bearer <token>`

Query Parameters

string

default:"day-ahead"

The market session to evaluate. Must be one of day-ahead, intraday-1, intraday-2, or intraday-3.

string

required

Start of the evaluation period, in YYYY-MM-DD format. Must be earlier than date_to.

string

required

End of the evaluation period, in YYYY-MM-DD format. The window between date_from and date_to may not exceed 90 days.

integer

When provided, restricts metric computation to a single hour of the day (0–23). Useful for isolating performance at peak or off-peak hours. Omit to aggregate across all 24 hours.

string

Filter results to a specific model version string (e.g. v2.4.1). Omit to include all versions active during the requested date range.

Request Example

The examples below request day-ahead accuracy metrics for a 30-day window.

curl -G https://app.nexenergie.ai/api/v1/metrics/accuracy \
  -H "Authorization: Bearer <token>" \
  --data-urlencode "market=day-ahead" \
  --data-urlencode "date_from=2024-04-01" \
  --data-urlencode "date_to=2024-04-30"

import requests

url = "https://app.nexenergie.ai/api/v1/metrics/accuracy"
headers = {"Authorization": "Bearer <token>"}
params = {
    "market": "day-ahead",
    "date_from": "2024-04-01",
    "date_to": "2024-04-30",
}

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

print(f"MAE:  {metrics['mae']} EUR/MWh")
print(f"RMSE: {metrics['rmse']} EUR/MWh")
print(f"MAPE: {metrics['mape']}%")

Response

A successful request returns HTTP 200 OK with a JSON object containing aggregate accuracy statistics and, optionally, a per-hour breakdown.

string

The market session the metrics apply to (e.g. "day-ahead").

string

The start of the evaluated period, in YYYY-MM-DD format.

string

The end of the evaluated period, in YYYY-MM-DD format.

string

The model version that produced the majority of forecasts within this date range. If multiple versions contributed, this reflects the dominant one.

integer

The total number of hourly data points used to compute the metrics. Each calendar day contributes up to 24 observations.

number

Mean Absolute Error in EUR/MWh — the average absolute deviation between forecast and settled price.

number

Root Mean Square Error in EUR/MWh — penalises large individual errors more heavily than MAE.

number

Mean Absolute Percentage Error expressed as a percentage — useful for comparing performance across markets with different price levels.

array

Present only when the hour query parameter is omitted. Contains one entry per hour of day (0–23), enabling you to identify which hours drive the most error.

Show hourly_breakdown items

integer

Hour of day (0–23) in local Spanish time (CET/CEST).

number

MAE for this specific hour across all days in the evaluation window, in EUR/MWh.

number

RMSE for this specific hour across all days in the evaluation window, in EUR/MWh.

number

MAPE for this specific hour across all days in the evaluation window, as a percentage.

Example Response

{
  "market": "day-ahead",
  "date_from": "2024-04-01",
  "date_to": "2024-04-30",
  "model_version": "v2.4.1",
  "n_observations": 720,
  "mae": 4.83,
  "rmse": 6.21,
  "mape": 8.14,
  "hourly_breakdown": [
    { "hour": 0, "mae": 3.91, "rmse": 5.02, "mape": 6.77 },
    { "hour": 1, "mae": 3.74, "rmse": 4.88, "mape": 6.42 },
    { "hour": 8, "mae": 5.60, "rmse": 7.34, "mape": 9.25 },
    { "hour": 20, "mae": 6.12, "rmse": 8.05, "mape": 10.41 }
  ]
}

Interpreting Results

Use the MAE ranges below as a practical reference for assessing forecast quality and calibrating downstream risk decisions.

MAE Range	Interpretation
< 5 EUR/MWh	Excellent — very low error, high confidence
5–10 EUR/MWh	Good — typical for OMIE day-ahead forecasting
10–15 EUR/MWh	Elevated — moderate caution advised
> 15 EUR/MWh	High — consider widening risk buffers

RMSE values will typically be 20–40% higher than MAE for the same period; a large gap between the two indicates occasional large spike errors worth investigating at the hourly level.

Error Responses

HTTP Status	Code	Description
`400`	`invalid_parameters`	A required parameter is missing, `date_from` ≥ `date_to`, or the window exceeds 90 days.
`401`	`unauthorized`	The `Authorization` header is missing or the token has expired.
`404`	`no_actuals_available`	Actual OMIE settlement prices are not yet available for the requested date range. This typically occurs for very recent dates.

Accuracy metrics are computed against official OMIE settled prices, which are published with a short delay after each session closes. Dates within the last 24–48 hours may return no data or partial data while OMIE settlement prices are still being published and reconciled.

Overview

Forecasts

Market Data

Accuracy & Metrics

GET /metrics/accuracy — Forecast Accuracy Statistics

Endpoint

Query Parameters

Request Example

Response

Example Response

Interpreting Results

Error Responses

​Endpoint

​Query Parameters

​Request Example

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​Example Response

​Interpreting Results

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Endpoint

Query Parameters

Request Example

Response

Example Response

Interpreting Results

Error Responses