The m.e Platform uses a unique AI-driven method to forecast energy & emission savings. Traditional energy-saving systems use a baselining method where energy use over a given time period with no controls is compared to a period with controls. This method has many variables that can influence the reported savings including seasonal shifts, daily weather, occupant working patterns and appliance energy behaviour. For this reason, the measurable.energy saving methodology uses the following principles:
Using this information from our data collection we then employ the following calculation as part of a dynamic algorithm on each individual socket:
Figure 1: The measurable.energy savings forecast calculation that forms part of a comprehensive AI algorithm.
The energy output is then lined up against the carbon intensity data published by the National Grid (Carbon Intensity API) to provide an emission reduction for that period in grams/kilograms.
Figure 2: Example of how the measurable.energy saving method captures dynamic changes to savings on a daily basis.
This method allows us to account for differences in daily appliance use and is not influenced by variables such as the seasons, weather changes or any changes to occupant patterns. There are however two scenarios that we cannot account for:
- If an appliance is scheduled to turn on (with another control system) during one of our off rulesets. The appliance would have come on during this time and we have made an energy saving by keeping it off, but we cannot see it, therefore it is not reported and we underpredict the saving.
- If an appliance is on/standby when the ruleset turns it off, but the appliance would have been controlled (with another control system) to turn off later. Learning the appliance behaviour helps us to mitigate for this but can result in an overprediction in a small number of edge cases.
The algorithms highlight sockets where it has not been possible to accurately calculate savings. These are removed from the reporting and returned when there is enough data to confirm accuracy.
It is important to note that any form of energy-saving reporting is a prediction whether it is comparing a baseline period or using AI or ML. measurable.energy are continually enhancing and optimising the algorithms we use to predict these energy savings and will update our reporting regularly to ensure that the most accurate possible predictions are always available to our customers.