> 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/suites/energy-and-sustainability-intelligence/energy-anomaly-detection/anomaly-detail-page.md).

# Anomaly detail Page

### Overview

The *Anomaly detail* page shows the full context of a detected anomaly.

Here, you can:

* review the main anomaly metrics
* inspect the anomaly in a chart
* view diagnostics and AI-generated explanations
* review the affected asset information
* validate the anomaly
* view related child anomalies

To open the page, navigate to [*Alert Center*](/collection/platform/alert-center.md) -> [*Anomaly Center*](/collection/suites/energy-and-sustainability-intelligence/energy-anomaly-detection/anomaly-center.md) and click on the arrow icon of an anomaly in the anomaly list.\
▷ The *Anomaly detail* page opens.

### Elements of *Anomaly detail*

<figure><img src="/files/j3zd6eSpeXXIORhu1Hzm" alt=""><figcaption></figcaption></figure>

<table data-header-hidden><thead><tr><th width="50.5999755859375">Marker</th><th width="197.60003662109375">Element</th><th>Description</th></tr></thead><tbody><tr><td><strong>[A]</strong></td><td>Main metrics</td><td>Shows the main anomaly values, such as financial impact, severity, validity, status information, deviation, predicted value, actual value, timeframe, and start and end time.</td></tr><tr><td><strong>[B]</strong></td><td>Anomaly chart</td><td>Shows the anomaly in the selected aggregation timeframe.</td></tr><tr><td><strong>[C]</strong></td><td>Diagnostics and AI analysis</td><td>Shows rule-based diagnostics, AI-generated explanations, and recommended actions.</td></tr><tr><td><strong>[D]</strong></td><td>Source information</td><td>Shows the asset information of the anomaly, including asset, asset type, attribute, and tags.</td></tr><tr><td><strong>[E]</strong></td><td>Validation</td><td>Lets you set the anomaly status to <strong>Confirmed</strong> or <strong>False</strong> and add a comment.</td></tr><tr><td><strong>[F]</strong></td><td>Nested anomalies</td><td>Shows the child anomalies related to the selected anomaly.</td></tr></tbody></table>

### Understand the anomaly chart

<figure><img src="/files/5TNG1ewdoYUD7CnWoF6b" alt=""><figcaption></figcaption></figure>

The chart **\[B]** shows the meter data in the aggregation timeframe of the anomaly.

The chart elements have the following meaning:

* **Light blue line**: The measured data of the meter
* **Red dots**: The detected anomaly points
* **Red background**: The duration of the anomaly
* **Green range**: The range in which the model expected healthy behavior

You can change the displayed timespan.\
The chart remains centered on the selected anomaly.

### Diagnostics and AI analysis

<figure><img src="/files/7S0Rl4ZQiTKvXEzuwmdI" alt=""><figcaption></figcaption></figure>

The section **\[C]** contains diagnostic and explanatory information about the anomaly.

It includes:

* **Diagnostics**\
  Rule-based diagnostics, including weather-based diagnostics and information about which asset types had the strongest influence on the anomaly.
* **AI explanation and recommended actions**\
  AI-generated explanation and recommended actions for the anomaly.

#### Generate or update the AI analysis

1. Click on **AI Analysis**. ▷ The explanation and recommended actions are generated or updated.

#### Open the AI deep dive

1. Click on **AI Deepdive**. ▷ A deeper analysis of the anomaly opens.

◈ *AI Deepdive* is available if the corresponding assistant module is installed.

### Validate the anomaly

In **\[E]**, you can validate the anomaly and add a comment.

1. Under **Validity status**, select:
   * **Confirmed**
   * **False**
2. If required, enter a comment.
3. Click on **Save**. ▷ The validation is updated.

◈ This feedback is considered by the system.\
◈ Marked false alarms help improve future anomaly handling and reduce repeated alerting of similar false positives.


---

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