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.
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 language | English |
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Qualification | Doctor of Philosophy |
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Award date | 1 Mar 2021 |
DOIs | |
Publication status | Unpublished - 2021 |