Open Access
| Issue |
E3S Web of Conf.
Volume 540, 2024
1st International Conference on Power and Energy Systems (ICPES 2023)
|
|
|---|---|---|
| Article Number | 08008 | |
| Number of page(s) | 8 | |
| Section | Energy Management System | |
| DOI | https://doi.org/10.1051/e3sconf/202454008008 | |
| Published online | 21 June 2024 | |
- Sharma, A. (2011). A comprehensive study of solar power in India and World. Renewable and Sustainable Energy Reviews, 15(4), 1767–1776.https://doi.org/10.1016/j.rser.2010.12.017 [CrossRef] [Google Scholar]
- Jurj, S.L., Rotar, R., Opritoiu, F., Vladutiu, M. (2020). Efficient Implementation of a Self-sufficient Solar-Powered Real-Time Deep Learning-Based System. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_7 [Google Scholar]
- M. Khodayar, S. Mohammadi, M. E. Khodayar, J. Wang and G. Liu, “Convolutional Graph Autoencoder: A Generative Deep Neural Network for ProbabilisticSpatio-Temporal Solar Irradiance Forecasting,” in IEEE Transactions on Sustainable Energy, vol. 11, no. 2, pp. 571–583, April 2020, doi: 10.1109/TSTE.2019.2897688 [CrossRef] [Google Scholar]
- A. Gensler, J. Henze, B. Sick and N. Raabe, “Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks,” 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary, 2016, pp. 002858–002865, doi: 10.1109/SMC.2016.7844673 [Google Scholar]
- X. Qing, Y. Niu, Hourlyday-aheadsolar irradiance predictionusingweatherforecasts by LSTM », Energy, 148 (2018), pp. 461–468https://doi.org/10.1016/j.energy.2018.01.177 [CrossRef] [Google Scholar]
- A. Fentis, L. Bahatti, M. Mestari and B. Chouri, “Short-term solar power forecasting using Support Vector Regression and feed-forward NN,” 2017 15th IEEE International New Circuits and Systems Conference (NEWCAS), Strasbourg, France, 2017, pp. 405–408, https://doi.org/10.1109/NEWCAS.2017.8010191 [Google Scholar]
- M L B., T Sripriya., B Muthuraj., D.S. Kumar, V Venkatesh., B.S. Sridevi, M.M.S. Krishna, K. Rajan, A. Diriba, (2022), “Deep Learning-Based Smart Hybrid Solar Water Heater Erection Model to Extract Maximum Energy”, International Journal of Photoenergy, Vol. 2022. doi:10.1155/2022/2943386 [Google Scholar]
- Rajesh G., Raajini X.M., Kritika N., Kavinkumar A., Sagayam K.M., Som M.M., Wahab M.H.A., (2022), “Achieving Longevity in Wireless Body Area Network by Efficient Transmission Power Control for IoMT Applications”, International Journal of Integrated Engineering, Vol. 14, no. 3, pp.80–89.doi:10.30880/ijie.2022.14.03.009 [Google Scholar]
- Balaji V., Sekar K., Duraisamy V., Uma S., Raghavendran T.S., (2015), “Performance analysis of energy management controller for stand alone solar power generation system using soft computing techniques”, Jurnal Teknologi, Vol. 76, no. 12,pp.111–117.doi:10.11113/jt.v76.5889 [Google Scholar]
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