In this article, you will learn how to visualise the anomalies calculated by Optimise in Analytics and, therefore, in Dashboards and reports. The article is organised as follows:
Introduction
Optimise anomalies can now be analysed not only within Optimise, but also directly in Analytics and visualised in Dashboards and Reports.
This allows you to combine anomaly insights with your other energy KPIs and build customised views to monitor abnormal consumption across your portfolio. It also allows you to get a summary of all the anomalies according to your business criteria directly in dashboards or reports. Optimise remains available for you to understand the details of the anomalies that might be interesting for you.
Using the Anomalies by Parameter preset in Analytics, you can visualise:
- The number of anomalies detected by Optimise.
- The energy impact associated with those anomalies.
- The economic impact associated with those anomalies.
You can also use the available filters and breakdowns to focus your analysis on the anomalies that matter most and understand where, when and why they are occurring.
Requirements
Before using anomaly data in Analytics, Dashboards or Reports, Optimise must be enabled for the datapoints you want to analyse. Only anomalies from datapoints configured in Optimise can be used in your analyses.
If you have not configured Optimise yet, see Getting started with Optimise to learn how to configure your datapoints and start detecting anomalies. Once Optimise is configured and anomalies are available, you can use them in Analytics, Dashboards and Reports.
The requirements to visualise anomalies are the same as to use the product Optimise. As a reminder the requirements are:
- Advanced and Ultimate licenses
- Hourly data
- Energy sources available:
- Active Energy
- Gas Energy or Volume
- Thermal Energy (cooling or heating)
- Water Volume
How does it work?
Optimise anomaly data is available in Analytics through the Anomalies by Parameter preset. To start analysing anomalies:
- Go to Analytics.
- Select the Anomalies by Parameter preset.
- Select the concept you want to visualise: Number of anomalies, Anomalies impact (kWh, m3) or Anomalies cost
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Choose which parameters you want to visualise in the data
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Breakdown: You can choose to break down the results by parameter. This is especially useful for pie charts.
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Apply the filters you need: Reliability, Day of the week, Anomalies' starting hour, Anomalies' duration, Anomalies' impact (kWh, m3), Anomalies' impact cost...
- Select the period you want to analyse.
Important comments:
- Multiple currencies: If your account contains multiple currencies, the total economic impact of anomalies can only be displayed when the selected data uses a single currency. Use the location hierarchy tree to filter the results and ensure that only one currency is included in the analysis.
- Number of anomalies and resolution: Since anomalies can span multiple hours, the Number of anomalies metric can only be displayed at daily resolution or higher. It is not available at hourly resolution.
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KPIs: Each KPI can display only one concept and unit. If you want to visualise metrics with different concepts or units, create a separate KPI for each one. For example:
- KPI 1 – Total number of anomalies: Can include anomalies from all energy sources (same unit, one concept).
- KPI 2 – Economic impact of anomalies: Can aggregate anomalies when a single currency is used. If multiple currencies are present, create a separate KPI for each currency or region.
- KPI 3 – Total anomaly impact: You can aggregate parameters that use the same unit. For example, Electricity, Gas Energy and Thermal Energy can be combined because they are measured in kWh. Similarly, Gas Volume and Water can be combined because they are measured in m³. Parameters with different units, such as Electricity (kWh) and Gas Volume (m³), cannot be combined in the same KPI.
- Breakdown: Breakdown is particularly useful for pie charts, where it allows you to split the results into different categories. It has no effect on bar or line charts, as each data series is already displayed individually.
- Location hierarchy: The location hierarchy also applies to Optimise data. You can filter by tag and navigate through the location hierarchy to restrict which locations, and therefore which anomalies, are included in your graphs.
How to create Dashboards & Reports
Adding anomalies to a Dashboard
To add Optimise anomaly data to a Dashboard, you must first create and save the analysis in Analytics.
- Go to Analytics.
- Select Anomalies by Parameter.
- Configure the analysis, including the metric, period, filters and breakdowns you want to use.
- Save the analysis.
- Export the saved analysis to your Dashboard.
- Select the Dashboard where you want the widget to appear.
The resulting widget will use the configuration you defined in Analytics.
This means that the analysis is not configured directly from the Dashboard. First create the analysis in Analytics, save it, and then export it to the required Dashboard.
Adding anomalies to a Report
For Reports, the workflow is slightly different.
In addition to creating an analysis in Analytics, saving it, and then adding it to a Report, you can also add an Anomalies by Parameter analysis directly from the Report configuration. Find out more in our Reports article.
Examples
The flexibility of Anomalies by Parameter allows you to create different analyses depending on what you want to monitor.
Monitor the evolution of anomalies
Visualise the number of anomalies over time to understand whether abnormal consumption is increasing or decreasing and identify periods with unusual behaviour.
Focus on the most relevant anomalies
Apply filters to exclude anomalies that are not relevant to your analysis and focus, for example, on anomalies with a particular impact, reliability, duration or status.
This can help reduce noise and focus the Dashboard on the anomalies that require attention. For example: KPI for Impact of anomalies that happened during the weekend:
Analyse when anomalies occur
Display anomalies in a heatmap to identify patterns:
Analyse the distribution of anomalies by parameter
By selecting the Breakdown by parameter, you can display the distribution of anomalies by energy source:
Dashboard proposals
- Distribution of anomalies over the last week and last month, by parameter
- Total number of anomalies over the last week and last month (across all parameters)
- Table breakdown of the impact of anomalies over the last month
- Impact of anomalies for each energy source over the last month, compared with the previous month
- Evolution of the number and impact of anomalies over the last month for electricity and water. This allows us to assess whether a small number of anomalies has a high impact, and vice versa.
- Heatmaps: Very useful for identifying patterns and visually highlighting where the impact of anomalies has been highest.