Publikasjoner og ansvarsområder
Prediksjon av værrelaterte feil i distribusjonsnettet
AIOPS – Hvordan velge ut KI prosjekter og gå fra prototyp til drift
MLOps – Taking AI from Concept to Impact
Impact of seasonal weather on forecasting of power quality disturbances in distribution grids
Power supply disruptions, including short-time disturbances, can lead to large direct and indirect financial losses. The ability to predict the risk of these disturbances allows for preventive actions and increases the reliability of the supply. This paper investigates the impact of using...
Applications of Big Data and Data Science in the Electricity Distribution Grid - State-of-the-art
Almost every household in Norway has a smart electricity meter installed. The advanced functionality of the meters and the large amounts of data transmitted from them have the potential to contribute to more than just a more accurate billing of customers. This report is a result from the ENERGYTICS...
Impact of the Temporal Distribution of Faults on Prediction of Voltage Anomalies in the Power Grid
Is it possible to reliably predict voltage anomalies in the power grid minutes in advance using machine learning models trained on large quantities of historical data collected by power quality analysers (PQA)? Very little previous research has been done on this topic. To investigate whether this is...
Predicting the Grid -- Severe Weather, Outages, and Power Quality Forecast
Automated Detection of Electric Vehicles in Hourly Smart Meter Data
Automated detection of EVs from smart meter data can provide important insights for DSOs about spatiotemporal EV charging patterns. However, smart meters typically provide only hourly measurements of consumption while most load disaggregation techniques require at least minute level data. We use...