Open Access
Issue
RAIRO-Oper. Res.
Volume 58, Number 4, July-August 2024
Page(s) 3501 - 3519
DOI https://doi.org/10.1051/ro/2024099
Published online 02 September 2024
  • M.T. Ahammed and I. Khan, Ensuring power quality and demand-side management through IoT-based smart meters in a developing country. Energy 250 (2022) 123747.1–123747.19. [CrossRef] [Google Scholar]
  • M. Gholami, O. Shahryari, N. Rezaei and H. Bevrani, Optimum storage sizing in a hybrid wind-battery energy system considering power fluctuation characteristics. J. Energy Storage 52 (2022) 104634.1–104634.10. [Google Scholar]
  • B. Carrera, M.K. Sim and J.Y. Jung, PvHybNet: a hybrid framework for predicting photovoltaic power generation using both weather forecast and observation data. IET Renew. Power Gener. 143 (2020) 2192–2201. [CrossRef] [Google Scholar]
  • G. Memarzadeh and F. Keynia, Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm. Electr. Power Syst. Res. 192 (2021) 106995.1–106995.14. [CrossRef] [Google Scholar]
  • A. Onno, N. Rodkey, A. Asgharzadeh, S. Manzoor, Z.J. Yu, F. Toor and Z.C. Holman, Predicted power output of silicon-based bifacial tandem photovoltaic systems. Joule 4 (2020) 580–596. [CrossRef] [Google Scholar]
  • Y. Wang, S. Luo and Z. Wang, Photovoltaic power prediction combined with popular learning and improved BP neural network. Comput. Simul. 39 (2022) 153–157. [Google Scholar]
  • J. Arevalo-Soler, E. Sanchez-Sanchez, E. Prieto-Araujo and O. Gomis-Bellmunt, Impact analysis of energy-based control structures for grid-forming and grid-following MMC on power system dynamics based on eigenproperties indices. Int. J. Electr. Power Energy Syst. 143 (2022) 108369.1–108369.15. [CrossRef] [Google Scholar]
  • T. Nasmark and J. Andersson, Proton stopping power prediction based on dual-energy CT-generated virtual monoenergetic images. Med. Phys. 48 (2021) 5232–5243. [CrossRef] [PubMed] [Google Scholar]
  • G. Raina, S. Sinha, G. Saini, S. Sharma, P. Malik and N.S. Thakur, Assessment of photovoltaic power generation using fin augmented passive cooling technique for different climates. Sustain. Energy Technol. Assess. 52 (2022) 102095.1–102095.17. [Google Scholar]
  • S. Ma, Cross-regional analysis of new energy consumption capacity. J. Beijing Inst. Technol. 29 (2020) 38–44. [Google Scholar]
  • A. Saeed, C. Li, Z. Gan, Y. Xie and F. Liu, A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution. Energy 238 (2022) 122012.1–122012.14. [CrossRef] [Google Scholar]
  • M. Pierro, M. De Felice, E. Maggioni, D. Moser, A. Perotto, F. Spada and C. Cornaro, Data-driven upscaling methods for regional photovoltaic power estimation and forecast using satellite and numerical weather prediction data. Solar Energy 158 (2017) 1026–1038. [CrossRef] [Google Scholar]
  • A. Gulagi, M. Ram and C. Breyer, Role of the transmission grid and solar wind complementarity in mitigating the monsoon effect in a fully sustainable electricity system for India. IET Renew. Power Gener. 14 (2020) 254–262. [CrossRef] [Google Scholar]
  • F. Rodríguez, A. Galarza, J.C. Vasquez and J.M. Guerrero, Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control. Energy 239 (2022) 122116.1–122116.13. [Google Scholar]
  • V. Repecho, N. Masclans and D. Biel, A comparative study of terminal and conventional sliding-mode startup peak current controls for a synchronous buck converter. IEEE J. Emerg. Sel. Topics Power Electron. 9 (2021) 197–205. [CrossRef] [Google Scholar]
  • M. Tajdinian, M. Allahbakhshi, M. Mohammadpourfard, B. Mohammadi, Y. Weng and Z. Dong, Probabilistic framework for transient stability contingency ranking of power grids with active distribution networks: application in post disturbance security assessment. IET Gener. Transm. Distrib. 14 (2020) 719–727. [CrossRef] [Google Scholar]
  • W.M. Ebrahimi, Retraining deep neural network with unlabeled data collected in embedded devices. J. Electron. 20 (2022) 55–69. [Google Scholar]
  • R. Thenmozhi, A.W. Nasir, V.K. Sonthi, T. Avudaiappan, S. Kadry, K. Pin and Y. Nam, An improved sparrow search algorithm for node localization in WSN. CMC-Comput. Mater. Continua 71 (2022) 2037–2051. [CrossRef] [Google Scholar]
  • H. Peng, Y. Wen, Q. Wang, S. Wang and L. Wu, Crack detection in eggs with multi-level wavelet transform and BP neural network. Trans. Chin. Soc. Agric. Mach. 40 (2009) 170–174. [Google Scholar]
  • Y. Qiu and L. Zhao, Impact resistance prediction of fuselage coatings based on improved neural network. Ordnance Mater. Sci. Eng. 45 (2022) 148–153. [Google Scholar]

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