Energy consumption in residential households can be reduced by up to 12% only by giving feedback to household residents on the way they spend their energy
As ecology, environmental protection and energy efficiency are hot topics nowadays, research efforts on energy savings are becoming more and more popular.
Energy consumption in residential households can be reduced by up to 12% only by giving feedback to household residents on the way they spend their energy and , more precisely, on which home appliances are using energy at each point in time.
However, it would be time consuming and cost-ineffective to provide meters for all of the appliances as well as impractical to connect them directly to the devices.
Therefore, within the InBetween project, we focused on developing a non-intrusive system which can anyway provide this information to residents and encourage them to adopt a more energy-efficient behavior.
In order to provide that necessary information, a simple solution to present current electrical usage of individual appliances is the so called “Intrusive Load Monitoring”, which consists in connecting smart meters (e.g. sensors for measuring the energy consumed) with every appliance at home.
Nowadays it is common to have a single meter at home that allows you to know your total energy consumption.
But InBetween innovation lies in the fact that we are using only the available measurements without the need of placing complex equipment in your home.
In short, our NILM service requires only one metering sensor per household – the one collecting the data for the total energy or power consumption.
Then, our advanced models based on Convolutional Neural Networks estimate the individual appliances’ activations and consumptions.
In the end, our results are stored in the InBetween platform and further presented to the users through the mobile app.
