LSTM neural network model for ultra-short-term distribution zone substation peak demand prediction

Ibrahim Anwar Ibrahim, M. J. Hossain

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

Abstract

The accurate prediction of the distribution load demand data is the corner stone of future planning of the power system networks and energy management strategies and policies. This paper presents a long short-term memory (LSTM) neural networks model to predict the distribution zone substation peak demand data in New South Wales state, Australia for 14 years and based on 15-minute intervals. The obtained results are compared with those obtained by feed-forward neural networks (FFNNs) and recurrent neural networks (RNNs) models. Three statistical performance evaluation, namely, the root-mean-square error (RMSE), mean bias error (MBE) and mean absolute percentage error (MAPE) are used to verify the effectiveness of the proposed model. The RMSE, MBE and MAPE of the LSTM neural network model are 1.2556%, 1.2201% and 2.2250%, respectively. In addition, the computational time is 12.3309 second which is faster than FFNNs and RNNs models. The results show the effectiveness of the proposed model over the aforementioned models in terms of accuracy and computational speed.

Original languageEnglish
Title of host publication2020 IEEE Power and Energy Society General Meeting (PESGM)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-5
Number of pages5
ISBN (Electronic)9781728155081
ISBN (Print)9781728155098
DOIs
Publication statusPublished - 2020
Event2020 IEEE Power and Energy Society General Meeting, PESGM 2020 - Montreal, Canada
Duration: 2 Aug 20206 Aug 2020

Publication series

Name
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2020 IEEE Power and Energy Society General Meeting, PESGM 2020
CountryCanada
CityMontreal
Period2/08/206/08/20

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