E3S Web Conf.
Volume 191, 20202020 The 3rd International Conference on Renewable Energy and Environment Engineering (REEE 2020)
|Number of page(s)||6|
|Section||Modern Electronic Technology and Application|
|Published online||24 September 2020|
- K. Mahmud, M. J. Hossain and J. Ravishankar, Peak-Load Management in Commercial Systems With Electric Vehicles, IEEE Systems Journal, vol. 13, no. 2, pp. 1872-1882 (2019) [Google Scholar]
- Q. Dang, Electric Vehicle (EV) Charging Management and Relieve Impacts in Grids, 2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG), Charlotte, NC (2018) [Google Scholar]
- D. Yu, M. P. Adhikari, A. Guiral, A. S. Fung, F. Mohammadi and K. Raahemifar, The Impact of Charging Battery Electric Vehicles on the Load Profile in the Presence of Renewable Energy, 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), Edmonton, AB, Canada (2019) [Google Scholar]
- AS. Al-Ogaili, TJT. Hashim, NA. Rahmat, et al, Review on Scheduling, Clustering, and Forecasting Strategies for Controlling Electric Vehicle Charging: Challenges and Recommendations, IEEE Access, vol. 7 (2018) [Google Scholar]
- J. Zhu, Z. Yang, M. Mourshed, Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches, Energies, MDPI, Open Access Journal, vol. 12(14) (2019) [Google Scholar]
- ISO 15118-2 (CD, 2017), Edition 2: Road vehicles – Vehicle to grid communication interface – Part 2: Network and application protocol requirement [Google Scholar]
- Q. Sun, J. Liu, X. Rong, et al, Charging load forecasting of electric vehicle charging station based on support vector regression, 2016 IEEE PES Asia- Pacific Power and Energy Engineering Conference (APPEEC, Xi’an, 2016) [Google Scholar]
- Okfalisa, I. Gazalba, Mustakim and N. G. I. Reza, Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification, 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta (2017) [Google Scholar]
- J. Unpingco, Python for Probability Statistics, and Machine Learning, 2, pp. 214-218 ( Springer , 2019) [Google Scholar]
- W. Deng, Y. Guo, J. Liu, A missing power data filling method based on improved random forest algorithm, Chinese Journal of Electrical Engineering, vol. 5 (4) (2019) [Google Scholar]
- VK. Jain, A. Phophalia, M-ary Random Forest, Pattern Recognition and Machine Intelligence, 8th International Conference, PReMI 2019, Tezpur, India, December 17-20, 2019, Proceedings, Part II, pp.161-169 (Springer, 2019) [Google Scholar]
- D. Paper, Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python, 1, pp. 110, 120 (Apress, 2020) [Google Scholar]
- A.V. Joshi, Machine Learning and Artificial Intelligence, 1, pp. 53-61 (Springer, 2020) [Google Scholar]
- G. Ke, Q. Meng T. Finely, et al, LightGBM: A Highly Efficient Gradient Boosting Decision Tree, Advances in Neural Information Processing Systems 30 (NIP 2017) [Google Scholar]
- F. Hutter, L. Kotthoff, J. Vanschoren, Automated Machine Learning: Methods, Systems, Challenges, 1, pp. 3-8 (Springer, 2019) [Google Scholar]
- A. Jindal, M. Singh, N. Kumar, Consumption- Aware Data Analytical Demand Response Scheme for Peak Load Reduction in Smart Grid. IEEE Trans. Ind. Electron. (2018) [Google Scholar]
- H. Kaneko, Beware of r2 even for test datasets: Using the latest measured y‐values (r2LM) in time series data analysis, Journal of Chemometrics 33.2 (2018) [Google Scholar]
- J. Zhu, Z. Yang, Y. Chang, A novel LSTM based deep learning approach for multi-time scale electric vehicles charging load prediction, 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia, Chengdu, 20. [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.