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CIM Issues #7040

Add attributes to time-series to record forecast uncertainty

Added by Tom Berry 23 days ago. Updated 2 days ago.

Status:
New
Priority:
Normal
Solution Version:
Breaking Change:
No
Breaking Change Description:
CIM Impacted Groups:
WG13, WG14, WG16, WG21
Requestor:
Tom Berry
Standard(s):

IEC 61970-301

Version:
Clause:
Sub-Clause:
Paragraph:
Table:
Origination Date:
10/29/2024
Origination ID:
Originally Assigned To:

Description

A known omission in the CIM models is a standard way of describing the confidence levels of a forecast.
Commercial services provide APIs with various methods.

A simple extension to the CIM could follow this example:

https://docs.meteoblue.com/en/weather-apis/forecast-api/forecast-data

Predictability
A single weather forecasts model cannot be optimized for all weather conditions and areas. meteoblue operates a large number of weather models and collects data from multiple national weather services. Some models are more suitable for complex alpine terrain, while other models calculate fog conditions more precise. By combining multiple forecast models with statistics and machine learning algorithms, meteoblue calculates a learning multi-model forecasts (mLM). A byproduct of this approach is the ability to estimate the accuracy of the current forecast for each location.
If the majority of forecast models predicts the same weather conditions for a given location and achieve consensus, a high predictability is indicated. The predictability is given in percent, as well as a predictability_class which is just a simpler representation for the percentage value.

Predictability 0-100 %
Predictability class 0 - 5 1 = very low, 5 = very high

Existing CIM models
Core::BasicIntervalSchedule used for load forecasts, generation forecasts
Environmental::Forecast

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