> 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/golden-reports.md). # Golden reports The Golden Report is myCoreAI's recurring performance report for buildings under AI control. It shows what the building used, what myCoreAI influenced, what was saved, and how indoor climate held up. New reports are generated monthly. Use this page for the concepts shared across all energy sources. Use the sub-pages for source-specific heuristics, assumptions, and fallback values. ## What the report covers For each building, the Golden Report breaks energy use into: * **District heating** * **District cooling** * **Electricity** For each energy source, the report shows: * **Total consumption** during the period. * **myCoreAI-controlled share** during the period. * **Savings at uptime** and **potential savings**. The report also includes: * **Energy efficiency** metrics. * **Indoor climate** compliance for temperature and CO₂. ## Shared concepts across all energy pages These concepts mean the same thing in heating, cooling, and electricity. ### Uptime Uptime is the share of the period when myCoreAI actively controlled the relevant systems. Low uptime lowers two things: * The **myCoreAI-controlled share** * The **savings at uptime** ### myCoreAI-controlled share This is the part of the energy source myCoreAI could influence. It is calculated as the relevant HVAC energy use, weighted by uptime. ### Baseline The baseline is the estimate of how the building would have operated without myCoreAI. It is based on the building's historical control logic. That usually means supply curves, setpoints, and similar pre-AI behavior. ### Efficiency Efficiency measures how myCoreAI performed against the baseline over the same controlled hours. It is calculated at component level, then aggregated for the reporting period. ### Savings at uptime Savings at uptime is the energy avoided during the hours myCoreAI was in control. It is the difference between estimated baseline use and actual use over those same hours. ### Potential savings Potential savings is the full-period version of savings at uptime. It scales the realized savings to 100% uptime. Use it to judge the upside from better uptime. ### Auxiliary consumption Auxiliary consumption is energy use outside the heating, cooling, and ventilation loads myCoreAI controls. It is shown separately so controlled and non-controlled energy do not get mixed together. ### Indoor climate compliance Indoor climate compliance shows how often the building stayed within its configured comfort bounds. Read it next to savings. Lower energy use should not come at the cost of comfort. ## Energy-source pages The energy pages focus on heuristics, assumptions, and source-specific calculation details: * [Heating](/evaluation-and-reporting/golden-reports/heating.md) — district-heating splits, tap-water estimate, and component assumptions. * [Cooling](/evaluation-and-reporting/golden-reports/cooling.md) — district-cooling splits, `Other` estimate, and component assumptions. * [Electricity](/evaluation-and-reporting/golden-reports/electricity.md) — ventilation-electricity split and scope limits. ## Portfolio reports Portfolio reports add a layer above the per-building view: * Aggregated energy use across buildings. * Portfolio-level efficiency metrics. * Cross-building comparisons. ## AI insights Each building also gets AI-generated insights. These point to performance patterns and remaining opportunities. They support the numbers. They do not replace them. ## How to read your report Start with **uptime**. Low uptime explains low realized savings fast. Then compare **savings at uptime** with **potential savings**. The gap shows the value left on the table. Read **indoor climate** beside savings. Comfort still matters. Then check the relevant energy page for the assumptions behind the number. ## Improving accuracy The biggest accuracy gains come from better inputs: * **Higher-resolution energy data** * **Building-specific energy splits** * **A real tap-water figure** for district heating Share these with your Myrspoven representative. --- # 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/golden-reports.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.