A Variational Autoencoder-Based Dimensionality Reduction Technique for Generation Forecasting in Cyber-Physical Smart Grids

Devinder Kaur, Shama Naz Islam, Md Apel Mahmud

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Modern energy systems often regarded as smart grid (SG) systems are cyber-physical systems (CPS) equipped with advanced metering and smart sensing devices, leading to a high-dimensional data generation in large volumes. To address this challenge, we propose a new variational autoencoder (VAE)- based dimensionality reduction technique for SG data to enable renewable energy generation forecasting with improved accuracy. The proposed method integrates bidirectional long short-term memory (BiLSTM) deep neural networks with variational inference, to generate an encoded representation of the high-dimensional time-series energy data. The encoded data is further utilized as low- dimensional representation of the original data for the application of energy forecasting, which leads to the reduced computational cost and more accurate forecasting results. The efficacy of the proposed VAE-BiLSTM method is evaluated using python programming and tensorflow library on the data traces taken from the Ausgrid solar generation dataset. Moreover, a comparative analysis of the proposed technique is presented with the benchmark autoencoder (AE) and VAE-based methods. Our result analysis illustrates that the proposed VAE-BiLSTM outperforms VAE-RNN, VAE-LSTM, as well as standard AE- based methods using evaluation metrics such as reconstruction error, pinball score, root-mean square error (RMSE), and mean absolute error (MAE).

Original languageEnglish
Title of host publication2021 IEEE International Conference on Communications Workshops (ICC Workshops 2021)
Place of PublicationPiscataway, USA
PublisherIEEE
Number of pages6
ISBN (Electronic)9781728194417
ISBN (Print)9781728194424
DOIs
Publication statusPublished - Jun 2021
Externally publishedYes
Event2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021 - Virtual, Online
Duration: 14 Jun 202123 Jun 2021

Publication series

NameProceedings of the IEEE International Conference on Communications Workshops (ICC Workshops)
PublisherIEEE
ISSN (Print)2164-7038
ISSN (Electronic)2694-2941

Conference

Conference2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021
CityVirtual, Online
Period14/06/2123/06/21

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