> For the complete documentation index, see [llms.txt](https://docs.myrspoven.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.myrspoven.com/ai-features/groupings.md). # Groupings myCoreAI controls indoor climate by sensor group, not by individual sensor. Each building is partitioned into groups tied to the HVAC system that influences them, and myCoreAI holds the group average temperature inside a target band. Control is smoother and less reactive than tracking each sensor in isolation. ## How groupings work Each group exposes three quantities to myCoreAI: * A **group average** — the mean of all indoor temperature sensors in the group. * An **average minimum** and **average maximum** — the means of the per-sensor lower and upper bounds across the group. * A scope tied to a specific HVAC asset, so a group corresponds to a heating circuit, air handling unit (AHU), or zone asset rather than an arbitrary set of rooms. myCoreAI compares the group average against the average min and max, and acts only when the group as a whole drifts outside the band. Individual sensors can sit outside their own limits without triggering a control change, as long as the group mean stays inside. ## Why this improves stability * **Collective control.** Limits apply to the group mean, not to each sensor. myCoreAI reacts to building-wide trends rather than a single noisy reading. * **Resilience to outliers.** A single sensor has little weight in the average. A meaningful share of the group has to move before the mean shifts. * **Seasonal balance.** Because limits apply to the average: * In a **20–23 °C** winter range, up to roughly half the sensors can read below 20 °C while the group mean still sits in the band. * In a **21–24 °C** summer range, up to roughly half can read above 24 °C before cooling is increased. The result is fewer short-cycle reactions to local disturbances and a steadier control signal overall. ## Where the limits come from Per-sensor minimums and maximums feeding the group averages come from one of two sources: * **Comfort bounds**, when comfort bounds are set explicitly for the building. * **Reference data**, when [AI Freedom](/ai-features/ai-freedom.md) is enabled. Bounds are derived from historical sensor behavior. ## How groups are defined Groups are not a free-form tagging system. myCoreAI assigns each indoor sensor to the heating or cooling system that most influences it, based on the building's signal mapping: * Buildings with **zone assets** (per-zone heating or cooling units): each zone asset forms its own group. Remaining sensors are grouped under the heating circuits. * Buildings heated mainly through ventilation: sensors are grouped by **air handling unit**. * Otherwise: sensors are grouped by **heating circuit** for heating and by **cooling circuit** for cooling. A building can have both heating groups and cooling groups, and the two sets can overlap. Groups outside myCoreAI's control scope — for example, circuits that are not written to — are excluded from the comfort penalty. ## Worked example An office served by two heating circuits, one feeding the open-plan floors and one feeding a set of corner offices. The corner offices include a south-facing room that runs warm on sunny afternoons. Without groupings, the warm reading pulls myCoreAI toward reducing supply temperature, which under-heats the rest of the circuit. With groupings, the south-facing sensor is one input to the corner-office group mean; myCoreAI keeps the group average inside 20–23 °C and the open-plan circuit stays unaffected. The warm room is handled as a local disturbance rather than a circuit-wide signal. --- # 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.myrspoven.com/ai-features/groupings.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.