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Christian Andre Andresen

Research Manager

Christian Andre Andresen

Research Manager

Christian Andre Andresen
Phone: 957 79 331
Email:
Department: Energy Systems
Office: Trondheim

Publications and responsibilities

Publication
https://www.sintef.no/en/publications/publication/?pubid=1832820

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...

Year 2020
Type Academic chapter/article/Conference paper
Publication
https://www.sintef.no/en/publications/publication/?pubid=1832993

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 season...

Year 2020
Type Academic chapter/article/Conference paper
Publication
https://www.sintef.no/en/publications/publication/?pubid=1832505

This paper studies the electricity consumption of 5 villas in the south of Norway and estimates the effect of utilizing batteries as a means to reduce peak load for each villa. High-resolution field data on the consumption pattern for the villas is presented. A simple battery model is utilized, and ...

Year 2020
Type Academic chapter/article/Conference paper
Publication
https://www.sintef.no/en/publications/publication/?pubid=1832900

The power system is changing rapidly, and new tools for predicting unwanted events are needed to keep a high level of security of supply. Large volumes of data from the Norwegian power grid have been collected over several years, and unwanted events as interruptions, earth faults, voltage dips and r...

Year 2020
Type Academic chapter/article/Conference paper
Publication
https://www.sintef.no/en/publications/publication/?pubid=1832811

There is a growing interest in applying machine learning methods on large amounts of data to solve complex problems, such as prediction of events and disturbances in the power system. This paper is a comparative study of the predictive performance of state-of-the-art supervised machine learning meth...

Year 2020
Type Academic chapter/article/Conference paper