Blar i SINTEF Open på forfatter "Hoffmann, Volker"
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Applications of Big Data and Data Science in the Electricity Distribution Grid - State-of-the-art
Ingebrigtsen, Karoline; Hoffmann, Volker (SINTEF Rapport;, Research report, 2020)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 ... -
Applications of Big Data and Data Science in the Electricity Distribution Grid - State-of-the-art
Ingebrigtsen, Karoline; Hoffmann, Volker (SINTEF Rapport;2020:00530, Research report, 2020)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 ... -
Automated Detection of Electric Vehicles in Hourly Smart Meter Data
Hoffmann, Volker; Fesche, Bjørn Ingeberg; Ingebrigtsen, Karoline; Christie, Ingrid Nytun; Punnerud Engelstad, Morten (Journal article; Peer reviewed, 2019)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 ... -
Big Data, Data Science, and Machine Learning
Hoffmann, Volker (Lecture, 2018) -
Energy Analytics - Opportunities for Energy Monitoring and Prediction with smart Meters
Ingebrigtsen, Karoline; Berre, Arne- Jørgen; Hoffmann, Volker (Journal article; Peer reviewed, 2017)By 2019, Norway will complete the national rollout of advanced metering systems (AMS) for all customers. Beyond near-time monitoring of voltage quality and frictionless billing of customers, such a rollout opens a host of ... -
Energy Analytics - Opportunities for Energy Monitoring and Prediction with smart Meters
Ingebrigtsen, Karoline; Berre, Arne- Jørgen; Hoffmann, Volker (Journal article; Peer reviewed, 2017)By 2019, Norway will complete the national rollout of advanced metering systems (AMS) for all customers. Beyond near-time monitoring of voltage quality and frictionless billing of customers, such a rollout opens a host of ... -
Impact of seasonal weather on forecasting of power quality disturbances in distribution grids
Michałowska, Katarzyna; Hoffmann, Volker; Andresen, Christian Andre (Chapter; Peer reviewed, 2020)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 ... -
Impact of seasonal weather on forecasting of power quality disturbances in distribution grids
Michałowska, Katarzyna; Hoffmann, Volker; Andresen, Christian Andre (Chapter, 2020)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 ... -
Impact of the Temporal Distribution of Faults on Prediction of Voltage Anomalies in the Power Grid
Tyvold, Torfinn Skarvatun; Torsæter, Bendik Nybakk; Andresen, Christian Andre; Hoffmann, Volker (Chapter, 2020)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 ... -
Incipient Fault Prediction in Power Quality Monitoring
Hoffmann, Volker; Michalowska, Katarzyna; Andresen, Christian Andre; Torsæter, Bendik Nybakk (Journal article; Peer reviewed, 2019)European and global power grids are moving towards a Smart Grid architecture. Supporting this, advanced measurement equipment such as PQAs and PMUs are being deployed. These generate vast amounts of data upon which machine ... -
Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid
Hoffmann, Volker; Torsæter, Bendik Nybakk; Rosenlund, Gjert Hovland; Andresen, Christian Andre (Peer reviewed; Journal article, 2022)With the advancing integration of fluctuating renewables, a more dynamic demand-side, and a grid running closer to its operational limits, future power system operators require new tools to anticipate unwanted events. ... -
The value of multiple data sources in machine learning models for power system event prediction
Hoffmann, Volker; Klemets, Jonatan Ralf Axel; Torsæter, Bendik Nybakk; Rosenlund, Gjert Hovland; Andresen, Christian Andre (Chapter; Peer reviewed, 2021)We describe a method for assessing the value of additional data sources used in the prediction of unwanted events (voltage dips, earth faults) in the power system. Using this method, machine learning models for event ...