Anomaly / Fraud Detection

You can foresee the risks that threat the supply security, take action to avoid these risks and educe revenue loss by using our technical / non-technical loss detection solution.

You can learn the electricity consumption breakdown of your home for each device and take action to increase energy efficiency with our energy disaggregation solution. Moreover this information will be helpful in the demand side management.

Technical / Non-Technical Loss Detection

Losses in power grids are divided into technical and non-technical losses. Technical losses are mostly due to power loss, while non-technical losses (NTL) are due to electricity theft i.e. illegal use of electricity, faulty meters or billing errors. NTL has always been a source of problems for electricity distribution companies, with emerging safety risks and immeasurable loss of revenue.

A complex analysis study should be carried out to reduce the loss and leakage rates in distribution companies. These analyzes should be planned meticulously, from customer consumption profile, transformer values ​​to the comments of field workers. As VUCA Analytics, we offer solutions to both technical and non-technical losses with our models that we have developed using artificial intelligence and machine learning techniques. We can develop these solutions based on LV and MV subscribers. In addition, we make a score study for each transformer, considering the behavior of the customers under the transformer within the transformer-based anomaly detection.

Energy Disaggregation

Energy disaggregation algorithm decomposes the total load of the house, read by smart meter, to the household appliances that consumes the energy. It is essential for disaggregation algorithm, obtains device-based consumption data, which help to increase energy efficiency and energy conservation, to require low computational power to work on any device and constantly. Using household energy disaggregation, detailed records of the use of household appliances allows us to offer user specific smart home solutions based on; determining which household appliances consume how much energy, determining the household appliances that cause energy inefficiency, and notifying the home appliances plugged in while away from home. In addition, energy disaggregation enables energy literacy, smart grid management, demand-side management and designing energy efficient buildings.