Proceedings of the 2nd International Conference on Consumer Technology and Engineering Innovation (ICONTENTION 2023)

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Comparison of Techniques for Long-Term Transformer Load Forecasting: A Systematic Literature Review

Authors
Dwigian Netha Putra1, *, Mochammad Firdian Ramadhani P2, Muchtar Ali Setyo Yudono3
1Electrical Engineering, Nusa Putra University Sukabumi, Sukabumi, Indonesia
2Electrical Engineering, Nusa Putra University Sukabumi, Sukabumi, Indonesia
3Electrical Engineering, Nusa Putra University Sukabumi, Sukabumi, Indonesia
*Corresponding author. Email: dwigian.netha_te22@nusaputra.ac.id
Corresponding Author
Dwigian Netha Putra
Available Online 13 May 2024.
DOI
10.2991/978-94-6463-406-8_7How to use a DOI?
Keywords
—deep learning; load forecasting; machine learning; systematic literature review; time series; transformer
Abstract

Forecasting transformer loads in the electricity sector can help plan the development of power plants, transmission networks, and consumer distribution by predicting future actions that will be taken by energy providers. There are several ways to anticipate the proper and accurate use of artificial intelligence (AI) technology, including time series forecasting and machine learning or deep learning. Using journals from the last five years, we will conduct a systematic evaluation of the literature on load forecasting techniques to identify the most effective approaches for predicting long-term loads. The best method for predicting long-term burden is the linear regression method, based on the findings of a comprehensive literature review study that has been conducted. The best data for long-term load forecasting includes load data from the last two years as well as several other factors such as temperature and humidity.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2nd International Conference on Consumer Technology and Engineering Innovation (ICONTENTION 2023)
Series
Advances in Engineering Research
Publication Date
13 May 2024
ISBN
978-94-6463-406-8
ISSN
2352-5401
DOI
10.2991/978-94-6463-406-8_7How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Dwigian Netha Putra
AU  - Mochammad Firdian Ramadhani P
AU  - Muchtar Ali Setyo Yudono
PY  - 2024
DA  - 2024/05/13
TI  - Comparison of Techniques for Long-Term Transformer Load Forecasting: A Systematic Literature Review
BT  - Proceedings of the 2nd International Conference on Consumer Technology and Engineering Innovation (ICONTENTION 2023)
PB  - Atlantis Press
SP  - 29
EP  - 33
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-406-8_7
DO  - 10.2991/978-94-6463-406-8_7
ID  - Putra2024
ER  -