A Systematic Literature Review of Electricity Load Forecasting using Long Short-Term Memory

Brain; Deregulation; Electric load forecasting; Electric power plant loads; Electric utilities; Learning algorithms; Statistical tests; Electricity load; Electricity load forecasting; Evaluation metrics; Load predictions; Long term planning; LSTM; Machine learning algorithms; Medium-term planning; R...

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Main Authors: Salleh N.S.M., Suliman A., J�rgensen B.N.
Other Authors: 54946009300
Format: Conference Paper
Published: Springer Science and Business Media Deutschland GmbH 2023
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spelling my.uniten.dspace-272262023-05-29T17:41:15Z A Systematic Literature Review of Electricity Load Forecasting using Long Short-Term Memory Salleh N.S.M. Suliman A. J�rgensen B.N. 54946009300 25825739000 7202434812 Brain; Deregulation; Electric load forecasting; Electric power plant loads; Electric utilities; Learning algorithms; Statistical tests; Electricity load; Electricity load forecasting; Evaluation metrics; Load predictions; Long term planning; LSTM; Machine learning algorithms; Medium-term planning; Review papers; Systematic literature review; Long short-term memory Research in electricity load prediction has contributed towards short-, medium-, and long-term planning for electricity power companies. One of the methods applied to perform prediction is machine learning. There are various types of dataset features, machine learning algorithms, and evaluation metrics utilised. This paper reviewed articles on electricity load prediction published in between 2019 and 2021. The review applied the systematic literature review method. In total, there were 368 articles were gathered from an online database, IEEE. The search was made based on combinations of keywords, i.e. short-term, electricity, load, demand, deep learning, forecast, time series, regression, and long short-term memory. From the collected articles, 25 articles were selected from a thorough examination of titles and abstracts. In the end, 11 complete materials were selected for final review. The review concentrated on: (i) common dataset feature and duration used, (ii) testing and validation strategies, and (iii) the evaluation metrics selected. The historical electricity load dataset was sufficient to perform electricity prediction. However, it was improved by adding independent variables into the dataset. RMSE and MAPE were the most used evaluation metrics in the reviewed articles. � 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Final 2023-05-29T09:41:15Z 2023-05-29T09:41:15Z 2022 Conference Paper 10.1007/978-981-16-8515-6_58 2-s2.0-85127630467 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127630467&doi=10.1007%2f978-981-16-8515-6_58&partnerID=40&md5=b783e922de00fa0a17f45e1175f20053 https://irepository.uniten.edu.my/handle/123456789/27226 835 765 776 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Brain; Deregulation; Electric load forecasting; Electric power plant loads; Electric utilities; Learning algorithms; Statistical tests; Electricity load; Electricity load forecasting; Evaluation metrics; Load predictions; Long term planning; LSTM; Machine learning algorithms; Medium-term planning; Review papers; Systematic literature review; Long short-term memory
author2 54946009300
author_facet 54946009300
Salleh N.S.M.
Suliman A.
J�rgensen B.N.
format Conference Paper
author Salleh N.S.M.
Suliman A.
J�rgensen B.N.
spellingShingle Salleh N.S.M.
Suliman A.
J�rgensen B.N.
A Systematic Literature Review of Electricity Load Forecasting using Long Short-Term Memory
author_sort Salleh N.S.M.
title A Systematic Literature Review of Electricity Load Forecasting using Long Short-Term Memory
title_short A Systematic Literature Review of Electricity Load Forecasting using Long Short-Term Memory
title_full A Systematic Literature Review of Electricity Load Forecasting using Long Short-Term Memory
title_fullStr A Systematic Literature Review of Electricity Load Forecasting using Long Short-Term Memory
title_full_unstemmed A Systematic Literature Review of Electricity Load Forecasting using Long Short-Term Memory
title_sort systematic literature review of electricity load forecasting using long short-term memory
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806428443533901824
score 13.222552