> For the complete documentation index, see [llms.txt](https://docs.buildings.ability.abb/collection/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.buildings.ability.abb/collection/platform/platform-core/baseline-and-factors/forecast-engine/create-a-new-forecast.md).

# Create a New Forecast

Create a forecast to generate a continually updated forecast attribute from a selected source attribute and optional contextual features.

Before you begin, [open the *Forecast Engine*](/collection/platform/platform-core/baseline-and-factors/forecast-engine/open-the-forecast-engine.md) and click on *Add Forecast*.

<figure><img src="https://2013058394-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOqGnkz52o2zVUsEGEZGk%2Fuploads%2Fgit-blob-466563cf69b972152fe33cc8d41c746fa39565de%2Fnew%20forecast.png?alt=media" alt="The New Forecast page with configuration fields A through P marked"><figcaption></figcaption></figure>

## Configuration Fields

<table data-header-hidden data-search="false"><thead><tr><th width="55.51995849609375">Marking</th><th width="118.36004638671875">Field</th><th>Description</th></tr></thead><tbody><tr><td><strong>[A]</strong></td><td><strong>Name</strong></td><td>Name of the forecast. This is also the displayed name of the forecast attribute created on the selected asset.</td></tr><tr><td><strong>[B]</strong></td><td><strong>Asset</strong></td><td>Asset that contains the target data and on which the forecast attribute is created.</td></tr><tr><td><strong>[C]</strong></td><td><strong>Target attribute</strong></td><td>Source attribute containing the time series to forecast.</td></tr><tr><td><strong>[D]</strong></td><td><strong>Function</strong></td><td>Aggregation applied to the source data before forecasting. Available functions include <strong>Average</strong>, <strong>Time-weighted average</strong>, <strong>Sum</strong>, <strong>Counter</strong>, <strong>CUSUM counter</strong>, <strong>Minimum</strong>, and <strong>Maximum</strong>.</td></tr><tr><td><strong>[E]</strong></td><td><strong>Interval</strong></td><td>Time interval over which source values are aggregated.</td></tr><tr><td><strong>[F]</strong></td><td><strong>Forecast Horizon Time</strong></td><td>Number of horizon units to forecast into the future.</td></tr><tr><td><strong>[G]</strong></td><td><strong>Forecast Horizon Unit</strong></td><td>Unit used for the forecast horizon. Together, <strong>[F]</strong> and <strong>[G]</strong> define how far ahead the forecast is calculated.</td></tr><tr><td><strong>[H]</strong></td><td><strong>Percentile</strong></td><td>Probability percentile calculated by this forecast. If no percentile is selected, the forecast defaults to 50%.</td></tr><tr><td><strong>[I]</strong></td><td><strong>Description</strong></td><td>Optional description of the forecast and its purpose.</td></tr><tr><td><strong>[J]</strong></td><td><strong>Feature asset</strong></td><td>Asset containing a feature attribute used as additional forecasting context.</td></tr><tr><td><strong>[K]</strong></td><td><strong>Feature attribute</strong></td><td>Attribute whose data can improve the forecast by describing a factor related to the target.</td></tr><tr><td><strong>[L]</strong></td><td><strong>Delete Row</strong></td><td>Removes the corresponding feature attribute row.</td></tr><tr><td><strong>[M]</strong></td><td><strong>Add additional feature attribute</strong></td><td>Adds another feature attribute row.</td></tr><tr><td><strong>[N]</strong></td><td><strong>Save and continue</strong></td><td>Saves the forecast and returns to the forecast list.</td></tr><tr><td><strong>[O]</strong></td><td><strong>Save and create a duplicate</strong></td><td>Saves the forecast and opens a copy as the basis for another forecast.</td></tr><tr><td><strong>[P]</strong></td><td><strong>Back</strong></td><td>Returns to the forecast list without saving the current changes.</td></tr></tbody></table>

## Configure the Forecast

1. Enter a unique forecast name under **\[A]**.\
   ◈ The system uses this name as the displayed name of the generated attribute.
2. Select the asset under **\[B]** and the target attribute under **\[C]**.
3. Select the aggregation function under **\[D]** and its interval under **\[E]**.\
   ◈ For example, **Average** with an interval of 15 minutes forecasts values based on 15-minute averages.
4. Enter the horizon length under **\[F]** and select its unit under **\[G]**.\
   ◈ A value of `1` with the unit **Days** forecasts one day into the future.
5. Select the percentile under **\[H]**.
6. If required, enter a description under **\[I]**.
7. If relevant contextual data is available, select a feature asset under **\[J]** and a feature attribute under **\[K]**.
8. To include more context, click on **\[M]** and configure another feature row.\
   ◈ To remove a row, click on **\[L]**.
9. Complete the configuration with one of the following actions:
   * Click on **\[N]** to save and return to the list.
   * Click on **\[O]** to save and create a duplicate.
   * Click on **\[P]** to return to the list without saving.

{% hint style="warning" %}
After saving, wait a few seconds and then refresh the page so that the generated forecast attribute becomes visible in the platform and on the asset.
{% endhint %}

## Understand Percentiles

A percentile forecast expresses uncertainty as a probability-based estimate rather than treating the future as one exact outcome.

* **10%**: Lower estimate; 10% of modeled outcomes are expected at or below this value.
* **50%**: Median estimate; half of modeled outcomes are expected below and half above this value.
* **90%**: Upper estimate; 90% of modeled outcomes are expected at or below this value.

Configuring several forecasts for the same target with different percentiles provides an operating range. Comparing a lower, median, and upper estimate can reveal possible spikes, show their likelihood, and provide a fuller view of expected healthy operation.

{% hint style="info" %}
A percentile is not a certainty. For example, a 95th-percentile forecast indicates that approximately 95% of modeled outcomes are expected at or below that value and approximately 5% above it.
{% endhint %}

## Select Useful Feature Attributes

Feature attributes provide context that may improve forecast accuracy. When forecasting energy consumption, useful features can include:

* outside temperature
* people count or occupancy
* equipment operating mode
* temperature setpoint
* another control value that influences demand

Choose features that are available for the relevant period and have a meaningful relationship with the target. Adding unrelated features does not necessarily improve accuracy.

After saving, the *Forecast Engine* creates the forecast attribute and updates it on each forecast iteration. The value written is the predicted value at the end of the configured horizon; the complete future trend is recalculated as new data arrives.


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