A Systematic Literature Review of Machine Learning Methods for Short-term Electricity Forecasting

Forecasting; Investments; Machine learning; Development investment; Energy prediction; Evaluation metrics; Long term planning; Machine learning methods; Metric evaluation; Resource planning; Systematic literature review; Learning algorithms

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Main Authors: Md Salleh N.S., Suliman A., Jorgensen B.N.
Other Authors: 54946009300
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-253362023-05-29T16:08:17Z A Systematic Literature Review of Machine Learning Methods for Short-term Electricity Forecasting Md Salleh N.S. Suliman A. Jorgensen B.N. 54946009300 25825739000 7202434812 Forecasting; Investments; Machine learning; Development investment; Energy prediction; Evaluation metrics; Long term planning; Machine learning methods; Metric evaluation; Resource planning; Systematic literature review; Learning algorithms Research in energy prediction is widely explored as it is used in long term planning like development investment and resource planning to estimating tariffs and analyzing and scheduling of distribution network. One of the methods applied in performing the forecasting is machine learning. There are many machine learning algorithms, dataset features, and evaluation metrics used. This paper offers to review articles on energy prediction published from 2016 until 2019. The review is made based on Systematic Literature Review method. A total of 119 articles were gathered from various sources such as IEEE, Science Direct and ResearchGate. The search made based on keywords such as machine learning, electricity, energy demand, forecast, and prediction. Based on the articles gathered, 31 articles were selected based on thorough examination on the title and abstracts. Six full materials are chosen for the final review. The review focused on i) standard dataset features chosen, ii) the machine learning algorithms applied and iii) the result based on evaluation metrics. Similarities found between the papers include the forecasting type, features selected, using various methods in performing the machine learning and applying multiple metric evaluations for a single dataset. The findings however show, the chosen machine learning algorithm and metric evaluation are different among the researchers and dataset size may influence the accuracy of the model generated. � 2020 IEEE. Final 2023-05-29T08:08:16Z 2023-05-29T08:08:16Z 2020 Conference Paper 10.1109/ICIMU49871.2020.9243603 2-s2.0-85097644199 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097644199&doi=10.1109%2fICIMU49871.2020.9243603&partnerID=40&md5=c9693745d22d704663d62da8d8271c3b https://irepository.uniten.edu.my/handle/123456789/25336 9243603 409 414 Institute of Electrical and Electronics Engineers Inc. 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 Forecasting; Investments; Machine learning; Development investment; Energy prediction; Evaluation metrics; Long term planning; Machine learning methods; Metric evaluation; Resource planning; Systematic literature review; Learning algorithms
author2 54946009300
author_facet 54946009300
Md Salleh N.S.
Suliman A.
Jorgensen B.N.
format Conference Paper
author Md Salleh N.S.
Suliman A.
Jorgensen B.N.
spellingShingle Md Salleh N.S.
Suliman A.
Jorgensen B.N.
A Systematic Literature Review of Machine Learning Methods for Short-term Electricity Forecasting
author_sort Md Salleh N.S.
title A Systematic Literature Review of Machine Learning Methods for Short-term Electricity Forecasting
title_short A Systematic Literature Review of Machine Learning Methods for Short-term Electricity Forecasting
title_full A Systematic Literature Review of Machine Learning Methods for Short-term Electricity Forecasting
title_fullStr A Systematic Literature Review of Machine Learning Methods for Short-term Electricity Forecasting
title_full_unstemmed A Systematic Literature Review of Machine Learning Methods for Short-term Electricity Forecasting
title_sort systematic literature review of machine learning methods for short-term electricity forecasting
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806424176052928512
score 13.222552