> For the complete documentation index, see [llms.txt](https://docs.net2grid.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.net2grid.com/net2grid-energy-ai-cloud/functionality/peer-and-self-comparison.md).
# Peer- & Self- Comparison
## Behavioral Benchmarking & Energy Awareness
To make energy consumption more accessible and actionable, NET2GRID EnergyAI® translates raw disaggregation insights into meaningful comparative reports on a weekly and monthly basis.
By applying these comparative metrics directly to specific appliance categories, consumers can easily identify opportunities for energy savings and gain a much clearer understanding of their true energy behavior.
***
## Self-Comparison
Self-comparison shows how the current week's or month's energy usage compares to historical periods for each specific disaggregated category.
* **Track category consumption over time:** See exactly how energy usage trends week-over-week or month-over-month.
* **Identify seasonal changes:** For example, easily observe if space heating consumption in October is significantly higher or lower than it was in September.
* **Monitor behavioral impact:** Track the measurable effect of newly adopted energy habits or technology upgrades on specific loads.
***
## Peer-Comparison
Peer-comparison benchmarks the consumer's energy usage against similar households with comparable profiles. This is done at the category level (e.g., comparing a user's laundry consumption against the laundry consumption of similar homes during the exact same period).
* **Compare against similar profiles:** Contextualize energy usage by seeing how it stacks up against highly comparable households.
* **Identify above-average categories:** Quickly spot which specific appliances are using more energy than the baseline norm.
* **Highlight efficiency potential:** Pinpoint exactly where the biggest opportunities for home improvement and energy savings lie.
### Smart Profile Matching
To deliver truly meaningful and accurate peer benchmarks, the algorithm does not use a one-size-fits-all approach. Instead, it dynamically considers the most relevant profile attributes for each specific category:
* For **space heating**, the platform evaluates postal code, country, property size, and property age, factors that directly influence thermal loss and heating patterns.
* For **EV charging**, attributes like property size and age are irrelevant and safely ignored, ensuring the benchmark remains strictly focused on vehicle charging behavior.
> **The Result:** By translating raw disaggregated data into relevant, contextual comparisons, this feature drives deep energy awareness, sustained engagement, and measurable behavioral change.
Self- and Peer-Comparison as shown in a utility app
***
## How we deliver this
These comparative insights are delivered as structured data reports on a weekly or monthly cadence. The reporting payloads include both historical self-comparison data and calculated peer benchmarks for every disaggregated category.
Peer- & Self-Comparison is available in the [Monthly - Home Energy Reports](/net2grid-energy-ai-cloud/delivery-configurations/monthly-home-energy-reports.md) and [Daily - Consumer Intelligence](/net2grid-energy-ai-cloud/delivery-configurations/daily-consumer-intelligence.md) delivery configurations, where you'll also find the link to the interface specifications.
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