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...
Saved in:
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Springer Science and Business Media Deutschland GmbH
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Tenaga Nasional |
| id |
my.uniten.dspace-27226 |
|---|---|
| record_format |
dspace |
| 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 |