Open Access
Issue
E3S Web Conf.
Volume 391, 2023
4th International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2023)
Article Number 01048
Number of page(s) 9
DOI https://doi.org/10.1051/e3sconf/202339101048
Published online 05 June 2023
  1. A Review of Machine Learning Models for Forecasting Electricity Consumption [Google Scholar]
  2. Jui-Sheng Chou, Duc-Son Tran, Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders, Energy, Volume 165, Part B, 2018, Pages 709–726, ISSN 0360-5442 [CrossRef] [Google Scholar]
  3. A. González-Briones, G. Hernández, J. M. Corchado, S. Omatu and M. S. Mohamad, “Machine Learning Models for Electricity Consumption Forecasting: A Review,” 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, Saudi Arabia, 2019, pp. 1–6 [Google Scholar]
  4. A 2019 study by Alsmadi, Al-Madi, and Al-Turjman. A short-term power consumption forecasting method based on machine learning. IEEE Access, 7, 141329–141338. [Google Scholar]
  5. In 2019, Wang, X., Li, Y., and Wang, K. An innovative extreme learning machine algorithm-based prediction model for power use. Energy 12, page 3283. [Google Scholar]
  6. Zhang, J., Gao, F., Liu, L., and Zou, Y. (2021). A thorough examination of short-term load prediction utilising machine learning methods. 14, no. 10 (energy), 2958. [Google Scholar]
  7. In 2020, Xu, X., Yang, Y., Jiang, L., and Wang, W. Forecasting electricity use based on upgraded extreme learning machine and meteorological data. 51162-51172. IEEE Access, 8. [Google Scholar]
  8. Almutairi, A. H., Alqahtani, A. M., & Alqahtani, M. A. (2020). Alotaibi, M. R. A overview of machine learning-based methods for forecasting power use. Sustainable Development, 12(8), 3381. [Google Scholar]
  9. Hu, M., Huang, and Chen, Y. (2020). A hybrid strategy combining machine learning andwavelet transform for estimating power usage. 20632-20644. IEEE Access, 8. [Google Scholar]
  10. Yin, J., Li, H., Zhang, X., and Liu, X. 2019 A method based on deep learning for predicting power usage in a smart grid. 12, 703 Energy [Google Scholar]
  11. Li, W., Li, J., Li, Z., and Li, Y. (2021). Prediction of electricity usage using an upgraded extreme learning machine optimised by adaptive chaotic particle swarm optimisation. Energy, 14, 82. [Google Scholar]
  12. Nair, N., and Chen, Z. (2021). A thorough evaluation of machine learning-based electricity demand forecasting. Applied Energy, 286. 116505. [Google Scholar]
  13. Zhang, J., Wang, L., Wang, W., and Wu, Y. (2019). A cutting-edge hybrid model integrating deep learning and ensemble learning for predicting power use. Energy, 12, 4082. [Google Scholar]
  14. Hong, Y., Chen, W., and Chen, J. (2021). Employing cutting-edge feature extraction techniques and machine learning algorithms to anticipate short-term power demand. Applied Energy, 296. 117511. [Google Scholar]
  15. Gandomi, A. H., Alavi, A. H., and Siahkolah (2019). Models of hybrid machine learningfor predicting power use. 12, 1362, Energies. [CrossRef] [Google Scholar]
  16. In 2020, Wang, Y., Zhao, L., Shi, Y., and Li, H. Using hybrid machine learning methodsto anticipate electricity use. IEEE Access, 8 (58283–58294). [Google Scholar]
  17. Zhang, Y., Li, Y., Wu, J., and Yang, X. (2019). Energy, 12(2), 208. Machine learning- based short-term forecasting of power use. [Google Scholar]
  18. Y. Liu, T. Lin, D. Zhao, and J. Chen (2019). LSSVM-based electricity consumption forecasting that is hybrid algorithm-optimized. 36(6), 5921–5932, Journal of Intelligent& Fuzzy Systems. [Google Scholar]
  19. Wang, J.; Yu, J. (2020). Short-term load forecasting using ensemble learning and machine learning techniques. Energy 13(3), 60 [Google Scholar]
  20. Y. Sri Lalitha, G. V. Reddy, K. Swapnika, et al., “Analysis of Customer Reviews using Deep Neural Network,” International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR), 2022, pp. 1–5, [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.