Data compression for smart distribution systems

Arfah Ahmad

Research output: ThesisDoctoral Thesis

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Abstract

The use of advanced metering in a distribution network generates a massive amount of data. However, the limited bandwidth in the communication networks poses a challenge in transmitting that data. Data compression is the best solution to this challenge. This research proposed multiresolution matrix factorization (MMF) as a compression method for smart grid data. The MMF
is applicable in compressing large-size data with low error rates and high speed. An adaptive forecasting framework via long short-term memory (LSTM) is proposed for on-line data transmission. The adaptive forecasting framework is proven to forecast accurately and able to save communication bandwidth.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
Supervisors/Advisors
  • Sreeram, Victor, Supervisor
  • Datta, Amitava, Supervisor
Award date1 Mar 2021
DOIs
Publication statusUnpublished - 2021

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