Open Access
Issue
E3S Web Conf.
Volume 358, 2022
5th International Conference on Green Energy and Sustainable Development (GESD 2022)
Article Number 02045
Number of page(s) 4
Section Regular Contributions
DOI https://doi.org/10.1051/e3sconf/202235802045
Published online 27 October 2022
  1. Mamun, A.A., et al. “A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models.” IEEE Access PP.99(2020):1–1. [Google Scholar]
  2. Mujeeb, S., et al. “Big Data Analytics for Load Forecasting in Smart Grids: A Survey.” International Conference on Cyber Security and Computer Science (ICONCS), 2018 2019. [Google Scholar]
  3. Bae, D.J., B.S. Kwon, and K.B. Song. “XGBoost- Based Day-Ahead Load Forecasting Algorithm Considering Behind-the-Meter Solar PV Generation.” Energies 15(2021). [Google Scholar]
  4. Shi, H., M. Xu, and R. Li. “Deep Learning for Household Load Forecasting - A Novel Pooling Deep RNN.” IEEE Transactions on Smart Grid (2017):1-1. [Google Scholar]
  5. Zhang, Y., et al. “Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network.” IEEE Transactions on Smart Grid (2019). [PubMed] [Google Scholar]
  6. Tsiakmaki, M., et al. “Fuzzy-based active learning for predicting student academic performance using autoML: a step-wise approach.” Journal of Computing in Higher Education (2021):1–33. [PubMed] [Google Scholar]
  7. Johnson, S.A., and S. Ananthakumaran. “Smart Digital Forensic Framework for Crime Analysis and Prediction using AutoML.” International Journal of Advanced Computer Science and Applications 12.3(2021). [CrossRef] [Google Scholar]
  8. Gerassis, S., et al. “AI Approaches to Environmental Impact Assessments (EIAs) in the Mining and Metals Sector Using AutoML and Bayesian Modeling.” Applied Sciences 11.17(2021):7914. [CrossRef] [Google Scholar]
  9. Jin, H., Song, Q., & Hu, X.. (2019). Auto-Keras: An Efficient Neural Architecture Search System. [Google Scholar]
  10. Zimmer, L., M. Lindauer, and F. Hutter. “Auto- PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL.” (2020). [Google Scholar]
  11. Erickson, N., et al. “AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data.” (2020). [Google Scholar]
  12. https://www.kaggle.com/c/global-energy-forecasting-competition-2012-load-forecasting (Accessed on April 15th, 2022) [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.