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Multi-step time series forecasting of electric load using machine learning models

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Multi-step forecasting is very challenging and there are a lack of studies available that consist of machine learning algorithms and methodologies for multi-step forecasting. It has also been found that lack of collaborations between these different fields is creating a barrier to further developments. In this paper, multi-step time series forecasting are performed on three nonlinear electric load datasets extracted from Open-Power-System-Data.org using two machine learning models. Multi-step forecasting performance of Auto-Regressive Integrated Moving Average (ARIMA) and Long-Short-Term-Memory (LSTM) based Recurrent Neural Networks (RNN) models are compared. Comparative analysis of forecasting performance of the two models reveals that the LSTM model has superior performance in comparison to the ARIMA model for multi-step electric load forecasting.
Original languageEnglish
Title of host publication17th International Conference on Artificial Intelligence and Soft Computing
Subtitle of host publicationICAISC 2018
EditorsLeszek Rutkowski, Rafał Scherer, Marcin Korytkowski, Witold Pedrycz, Ryszard Tadeusiewicz, Jacek Zurada
PublisherSpringer Nature
Pages148-159
Number of pages12
ISBN (Electronic)978-3-319-91253-0
ISBN (Print)978-3-319-91252-3
DOIs
StatePublished - Jun 2018
EventInternational Conference on Artificial Intelligence and Soft Computing - Mercure Zakopane Kasprowy Hotel, Zakopane, Poland
Duration: 3 Jun 20187 Jun 2018
Conference number: 17th
http://icaisc.eu/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10841
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Artificial Intelligence and Soft Computing
Abbreviated titleICAISC 2018
CountryPoland
CityZakopane
Period3/06/187/06/18
Internet address

Documents

  • 5234

    Rights statement: The final authenticated version is available online at: http://dx.doi.org/10.1007%2F978-3-319-91253-0_15.

    Accepted author manuscript (Post-print), 389 KB, PDF-document

    Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 11/05/19

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