Abstract
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.
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Acknowledgements
The publication of this paper was funded by URND TNB Seeding Fund: U-TE-RD-20-08. The authors would like to thank the Institute of Informatics and Computing in Energy (IICE), Universiti Tenaga Nasional (UNITEN) for providing a platform to collaborate with the Center for Energy Informatics, Southern Denmark University (SDU).
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Salleh, N.S.M., Suliman, A., Jørgensen, B.N. (2022). A Systematic Literature Review of Electricity Load Forecasting using Long Short-Term Memory. In: Alfred, R., Lim, Y. (eds) Proceedings of the 8th International Conference on Computational Science and Technology. Lecture Notes in Electrical Engineering, vol 835. Springer, Singapore. https://doi.org/10.1007/978-981-16-8515-6_58
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DOI: https://doi.org/10.1007/978-981-16-8515-6_58
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