The Fraud Pattern Detection service employs advanced machine learning to uncover and classify energy fraud, providing valuable insights on magnitude, duration, and culprits. The objective is to optimize field inspections to reduce financial losses related to fraud detection.
Addressing target audiences and expressing needs
- Business partners – SMEs, Entrepreneurs, Large Corporations
- Incubators / Accelerators
- Business Angels
·To raise awareness in population
·Grants and Subsidies
·Company interested in commercializing the product
·Other blended financing
- Other Actors who can help us fulfil our market potential
- Research and Technology Organisations
- Private Investors
R&D, Technology and Innovation aspects
Prototype system demonstration in an operational environment, through project pilots. We will keep on implementing additional functionalities to our product, with the objective of increasing the TRL within the next years.
Our algorithm does not foresee scalability problems, as it can effectively adapt to the changes that distribution networks may undergo. This ensures that the service remains robust and efficient even as the grid grows in size, making it ideal for both small and large-scale energy infrastructures. Having been tested in two different countries, it has been found to work and a joint dataset can be made. It should be studied further to see if there are patterns of fraud that change from country to country.
The solution has been tested in two pilots in two different countries. Only by having data from meters and transformer stations can the results be replicated. In order to extend the study and its knowledge, the fraud base should be expanded with more cases, preferably from different countries and of different types. This service can be replicated, each country has its own specificities and GDPR restrictions. However, no market blocker has been identified so far.
During the development of the service, it has been seen that over the years, the types of fraud change, but do not disappear. To improve the integration of renewable energies in smart grids, it is important to have knowledge and observability of what happens in these, and that implies having frauds under control and reducing them to levels close to zero. Thus, even if fraud management improves, it will continue to exist and a service that learns from the patterns that exist can be very valuable.
- Global
