> 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/evaluation-and-reporting/climate-compensated.md). # Climate compensation Heating and cooling energy use is dominated by outdoor conditions. A cold January looks worse than a mild one even when the building runs identically. Climate compensation removes the weather component so periods can be compared on a like-for-like basis — month against month, winter against winter, and a building against itself before and after myCoreAI took control. ## What climate compensation does myCoreAI normalizes measured energy use against an outdoor-temperature reference. Instead of reporting raw kWh, it reports energy *per unit of heating demand*. Weather largely cancels out, and the remainder reflects the building and its control strategy. The principle is applied in three places: * **Per-building efficiency metrics** comparing AI-on against AI-off periods at equivalent outdoor temperatures. * **Regression-based savings estimates** for the heating season — energy is fit against outdoor temperature (or Heating Degree Days) separately for each control mode, and the gap between fits is the saving. * **Reference comparisons** against normalized consumption for similar buildings. ## Heating Degree Days (HDD) HDD is the standard normalization input. For each day, myCoreAI computes the gap between the building's base temperature (the outdoor temperature above which no heating is needed) and the day's average outdoor temperature, summing only the hours where outdoor temperature is below the base. A cold day produces a high HDD; a mild day produces a low one; a warm day produces zero. Energy divided by HDD expresses consumption per degree of heating demand — comparable across periods and weather conditions. Cooling Degree Days work symmetrically for district cooling. ## Resolution and accuracy | Resolution | Method | Result | | --------------- | -------------------------------------------- | --------------------------------------------------------------------------------- | | Hourly or daily | Regression against outdoor temperature / HDD | Cleanest normalization; short cold snaps do not distort the fit | | Monthly | National normalization factors | Works for top-level reporting; cannot isolate which week or system drove a change | Higher-resolution energy data is the single biggest improvement to climate-compensated reporting. ### Worked example A district-heated office in central Sweden consumes 180 MWh in a cold January and 140 MWh the following January under myCoreAI control. Raw figures suggest a 22% reduction. After myCoreAI normalizes both months against HDD using the building's base temperature, the second January's compensated consumption is 155 MWh — a 14% reduction once the milder weather is accounted for. The 14% figure is the one that reflects control performance; the 22% figure conflates weather and control. ## Where compensated figures appear * The **savings analysis** view in myPortal plots energy against outdoor temperature directly, with separate trend lines for AI-on and AI-off periods — a visual form of the same normalization, with weather on the x-axis. Raw, uncompensated consumption remains available in meter readings, invoices, and exports. Raw shows what the building consumed; compensated shows how it performed once weather is removed. --- # 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/evaluation-and-reporting/climate-compensated.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.