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

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dc.citedby4
dc.contributor.authorMd Salleh N.S.en_US
dc.contributor.authorSuliman A.en_US
dc.contributor.authorJorgensen B.N.en_US
dc.contributor.authorid54946009300en_US
dc.contributor.authorid25825739000en_US
dc.contributor.authorid7202434812en_US
dc.date.accessioned2023-05-29T08:08:16Z
dc.date.available2023-05-29T08:08:16Z
dc.date.issued2020
dc.descriptionForecasting; Investments; Machine learning; Development investment; Energy prediction; Evaluation metrics; Long term planning; Machine learning methods; Metric evaluation; Resource planning; Systematic literature review; Learning algorithmsen_US
dc.description.abstractResearch 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9243603
dc.identifier.doi10.1109/ICIMU49871.2020.9243603
dc.identifier.epage414
dc.identifier.scopus2-s2.0-85097644199
dc.identifier.spage409
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097644199&doi=10.1109%2fICIMU49871.2020.9243603&partnerID=40&md5=c9693745d22d704663d62da8d8271c3b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25336
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020
dc.titleA Systematic Literature Review of Machine Learning Methods for Short-term Electricity Forecastingen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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