By adapting the district heating use (meter_effect) during critical occasions during a day when there is a high load, you can reduce the climate impact. The idea is that by reducing the use of district heating when there is the most pressure on the system, you can reduce the climate impact by avoiding using less climate-friendly fuels.This dataset describes how, by artificially signaling a lower outdoor temperature (outdoor temp_offset) than the actual outdoor temperature (outdoor temp), the system is "tricked" into thinking that it is warmer outside. But to ensure that it does not get too cold inside and sacrifice comfort in the building, there is a budget / limit, which is controlled by the temperature sensors in the property. The property in this dataset is Ålidhem Healthcenter in Umeå.This dataset can be combined with the dataset Matematikgränd & Ålidhem HC peakload shaving, as the three buildings together form a grid. To relate the outcome to forecast values ​​for the entire grid, see the dataset Peakload shaving forecast and actual