- This event has passed.
PhD-defence: Analysis of High Frequency Smart...
January 30, 2019 @ 13:00 - 16:00
Alexander Tureczek will be defending his PhD ”Analysis of High Frequency Smart Meter Energy Consumption Data” Wednesday, 30th of January 2019, at 13:00. Download programme here
The PhD thesis is part of the CITIES-project.
Principal supervisor: Senior Researcher Per Sieverts Nielsen, DTU Management Engineering
Co supervisors: Professor Henrik Madsen, DTU Compute
Examiners: Senior Researcher Geraldine Henningsen, DTU Management
Senior Quantitative Developer Fannar örn Thordarson, Ørsted
Professor Kjell Sand, Norwegian University of Science and Technology NTNU
There will be a reception in building 426 afterwards, in the common room downstairs.
From the abstract: As society moves towards increasing electrification in areas such as transportation, the future peak electricity demand may very well exceed the capacity of the electricity grid. Consumption flexibility is expected to play an important role in peak shaving and smart meters can help analyze demand. Electricity smart meters are capable of recording consumption at very high frequency, down to the minute. These recordings allow for unprecedented consumption insights and identification of consumption patterns and flexibility. This thesis investigates the ability of electricity smart-meter consumption data to be used for consumption clustering to identify consumer types and enable diverse tariff structures and thus incentivize flexible consumption patterns.
The thesis concludes that smart-meter data can be applied to identify consumption clusters, but the current prevailing methodology produce academically viable clusters with limited practical applicability. There are structures in the data that the methodology currently applied are unable to manage e.g. reduce the within cluster variance to such a degree that the clusters are uniquely defined and identifiable. Further research into methods for time series clustering is needed to control the cluster variance and enable distinct consumption clusters.