Result description
This project employs regression techniques to predict real-time individual appliance power consumption, creating generalized models applicable to any household with metering data. This approach clarifies electrical measurement information for end-users, indicating appliance performance more accurately. Additionally, these generalized models support demand response campaigns by providing consumption curves for flexible assets.
Moreover, OPENTUNITY’s NILM also calculates the dynamic energy certification of the appliance. The European Commission defines a common procedure to calculate the label for each appliance. These labels often involve annual or per-cycle energy consumption values, calculated during testing periods by manufacturers under specific conditions. These theoretical values can be compared with real values from submetering or NILM calculations, providing insights into appliance performance using energy labeling.
Addressing target audiences and expressing needs
- To raise awareness and possibly influence policy
- Use of research Infrastructure
- Collaboration
- Raise awarenes about the capabilities of the software to reach ESCOs and retailers aiming to include this features in their own apps.
- Reach more datasets to increase even more the accuracy of the results.
- Collaboration with ESCOs and retailers and small consumers for more feedback and potential use from their side.
- Public or private funding institutions
- EU and Member State Policy-makers
- Research and Technology Organisations
R&D, Technology and Innovation aspects
We have evaluated the NILM in testing environments. We are now starting the actual demonstration in real households and then we will evaluate the results with the F1 score parameter and get feedback from households.
Algorithms are scalable to every average household. Large energy consumers like buildings or big households are not that appropriate.
It can be replicable to every region as long as they have Smart meters in place.
Previous experiences on NILM indicates energy reduction of 10% in households. Since the maintenance of the tool is small, the costs can be covered by the reduction of energy bills of the households.
