EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 540 (2024) E3S Web of Conf., 540 (2024) 10025 References
Open Access
Issue
E3S Web of Conf.
Volume 540, 2024
1st International Conference on Power and Energy Systems (ICPES 2023)
Article Number 10025
Number of page(s) 16
Section Grid Connected Systems
DOI https://doi.org/10.1051/e3sconf/202454010025
Published online 21 June 2024
  1. Yadoo, Annabel, and Heather Cruickshank. “The role for low carbon electrification technologies in poverty reduction and climate change strategies: A focus on renewable energy mini-grids with case studies in Nepal, Peru and Kenya.” Energy policy 42 (2012): 591–602. [CrossRef] [Google Scholar]
  2. Zhou, Hangxia, Qian Liu, Ke Yan, and Yang Du. “Deep learning enhanced solar energy forecasting with AI-driven IoT.” Wireless Communications and Mobile Computing 2021 (2021): 1–11. [Google Scholar]
  3. Yang, Dazhi, Weixing Li, Gokhan Mert Yagli, and Dipti Srinivasan. “Operational solar forecasting for grid integration: Standards, challenges, and outlook.” Solar Energy 224 (2021): 930–937. [CrossRef] [Google Scholar]
  4. Wen, Lulu, Kaile Zhou, Shanlin Yang, and Xinhui Lu. “Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting.” Energy 171 (2019): 1053–1065. [CrossRef] [Google Scholar]
  5. Widodo, D. A., N. Iksan, and E. D. Udayanti. “Renewable energy power generation forecasting using deep learning method.” In IOP Conference Series: Earth and Environmental Science, vol. 700, no. 1, p. 012026. IOP Publishing, 2021. [Google Scholar]
  6. Said, Yahia, and Abdulaziz Alanazi. “AI-based solar energy forecasting for smart grid integration.” Neural Computing and Applications 35, no. 11 (2023): 8625–8634. [CrossRef] [Google Scholar]
  7. Abou Houran, Mohamad, Syed M. Salman Bukhari, Muhammad Hamza Zafar, Majad Mansoor, and Wenjie Chen. “COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications.” Applied Energy 349 (2023): 121638. [CrossRef] [Google Scholar]
  8. Rai, Amit, Ashish Shrivastava, and Kartick C. Jana. “Differential attention net: Multidirected differential attention based hybrid deep learning model for solar power forecasting.” Energy 263 (2023): 125746. [CrossRef] [Google Scholar]
  9. Mohammad, Ashif, and Farhana Mahjabeen. “Revolutionizing Solar Energy with AIDriven Enhancements in Photovoltaic Technology.” BULLET: JurnalMultidisiplinIlmu 2, no. 4 (2023): 1031–1041. [Google Scholar]
  10. Munsif, Muhammad, Min Ullah, U. Fath, Samee Ullah Khan, Noman Khan, and Sung Wook Baik. “CT-NET: A Novel Convolutional Transformer-Based Network for Short-Term Solar Energy Forecasting Using Climatic Information.” Computer Systems Science & Engineering 47, no. 2 (2023). [Google Scholar]
  11. Mansouri, Nouha, Abderezak Lashab, Dezso Sera, Josep M. Guerrero, and Adnen Cherif. “Large photovoltaic power plants integration: A review of challenges and solutions.” Energies 12, no. 19 (2019): 3798. [CrossRef] [Google Scholar]
  12. Brummer, Benoit, and Christophe De Vleeschouwer. “Natural image noise dataset.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 0–0. 2019. [Google Scholar]
  13. Peng, Jiang-Zhou, Nadine Aubry, Shiquan Zhu, Zhihua Chen, and Wei-Tao Wu. “Geometry and boundary condition adaptive data-driven model of fluid flow based on deep convolutional neural networks.” Physics of Fluids 33, no. 12 (2021). [Google Scholar]
  14. Koenen, Niklas, and Marvin N. Wright. “Interpreting Deep Neural Networks with the Package Insight.” arXiv preprint arXiv:2306.10822 (2023). [Google Scholar]
  15. Chen, Yushi, Hanlu Jiang, Chunyang Li, Xiuping Jia, and Pedram Ghamisi. “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks.” IEEE transactions on geoscience and remote sensing 54, no. 10 (2016): 6232–6251. [CrossRef] [Google Scholar]
  16. Saritha G. et.al, Cross layer optimization for wireless network (WIMAX), Middle - East Journal of Scientific Research, V-16, I-12, PP:1786–1789, 2013. [Google Scholar]
  17. Unisa, S. A., & Gowda, K. J. (2022). Estimating CSI for Future Generation MIMO Networks Using Deep Learning Techniques and its Applicability to Varied Environments. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 13(4), 124–136. [CrossRef] [Google Scholar]
  18. Anzy, Laith & Abdullah, Ahmed & Aquraishi, Ali. (2023). IoT Cloud System Based Dual Axis Solar Tracker Using Arduino. Journal of Internet Services and Information Security. 3. 193–202. 10.58346/JISIS.2023.I2.012. [CrossRef] [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.