> 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/enriching-household-profiles.md). # Enriching Household Profiles ## Overview Occasionally, profile information may be unavailable, or consumers may omit important household attributes or fail to update them over time. This lack of accurate data, such as whether a household owns an Electric Vehicle (EV), operates electric heating or cooling systems, or generates power through solar PV or battery storage, can significantly affect the accuracy of energy disaggregation and the relevance of derived insights. To address this, NET2GRID EnergyAI® continuously monitors household energy data to enrich and maintain complete, up-to-date profiles. By analyzing high-resolution smart-meter time series, it detects the presence and operation of major energy assets including EVs, Solar PV, Battery systems, and Heating and Cooling (HVAC / Heat Pumps). This continuous enrichment process ensures that household profiles evolve in line with real consumption behavior, enabling more accurate attribution of energy use across appliance categories. The result is clearer disaggregation, more personalized insights, and stronger operational intelligence for energy retailers. *** ## How it works The algorithm behind Enriching Household Profiles powered by NET2GRID EnergyAI® is designed to improve household data accuracy by continuously identifying discrepancies between declared profiles and real energy consumption behavior. It leverages smart meter data to detect missing or outdated information about major Distributed Energy Resources (DERs) such as EVs, Solar PV systems, Battery storage, and Heating and Cooling systems. The system learns to recognize distinctive consumption and generation patterns associated with these assets. For example, recurring overnight charging cycles indicate EV ownership, temperature-correlated usage signals electric heating or cooling, and midday export patterns reveal solar PV generation. When these patterns are detected with high confidence, the corresponding household profiles are enriched or corrected to include the inferred appliances or assets. This process operates in a continuous feedback loop, where new smart meter data is analyzed as it streams in. Profiles are dynamically refined to reflect changes in appliance ownership or shifts in energy behavior, ensuring they remain representative over time. *** ## How we deliver this The enrichment insights are compiled into a clear, structured profile update, ready to be synced directly into your CRM, data warehouse, or consumer-facing applications. Enriching Household Profiles is available in the [Monthly - Grid Intelligence](/net2grid-energy-ai-cloud/delivery-configurations/monthly-grid-intelligence.md), [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. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.net2grid.com/net2grid-energy-ai-cloud/functionality/enriching-household-profiles.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.