EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 405 (2023) E3S Web of Conf., 405 (2023) 02011 References
Open Access
Issue
E3S Web of Conf.
Volume 405, 2023
2023 International Conference on Sustainable Technologies in Civil and Environmental Engineering (ICSTCE 2023)
Article Number 02011
Number of page(s) 12
Section Renewable Energy & Electrical Technology
DOI https://doi.org/10.1051/e3sconf/202340502011
Published online 26 July 2023
  1. M. Abdel-Nasser, K. Mahmoud, M. Lehtonen, Reliable Solar Irradiance Forecasting Approach Based on Choquet Integral and Deep LSTMs, in IEEE Transactions on Industrial Informatics, 17, 1873 (2021) [Google Scholar]
  2. B. Brahma, R. Wadhvani, Solar Irradiance Forecasting Based on Deep Learning Methodologies and Multi-Site Data. Symmetry, 12, 1830 (2020) [CrossRef] [Google Scholar]
  3. B. Jeon, EJ. Kim, Next-Day Prediction of Hourly Solar Irradiance Using Local Weather Forecasts and LSTM Trained with Non-Local Data, Energies, 13, 5258 (2020) [CrossRef] [Google Scholar]
  4. W. Wang, D. Yang, T. Hong, J. Kleissl, An archived dataset from the ECMWF Ensemble Prediction System for probabilistic solar power forecasting, Solar Energy, 248, 64 (2020) [Google Scholar]
  5. O. Oluwafemi, O.S. Olusola, E. Israel, A. Babatunde, Autoregressive neural network models for solar power forecasting over Nigeria, JSER, 7, 983 (2022) [Google Scholar]
  6. Zarei, N. Ghaffarzadeh, Optimal Demand Response-based AC OPF Over Smart Grid Platform Considering Solar and Wind Power Plants and ESSs with Short-term Load Forecasts using LSTM, JSER, 8, 1367 (2023) [Google Scholar]
  7. M. Khademi, A. Nikookar, M. Moadel, H. Aryan, Forecasting of PV Output Power in Cloudy Conditions by LOLIBEE, MLP-ABC and MLP Algorithms, JSER, 2, 38 (2017) [Google Scholar]
  8. P. Kumari, D. Toshniwal, Extreme gradient boosting and deep neural network-based ensemble learning approach to forecasts hourly solar irradiance. JCP, 279, 1 (2021) [Google Scholar]
  9. Gupta, K. Gupta, S. Saroha, Short term solar irradiation prediction framework based on EEMD-GA-LSTM Method, SPEE, 41, 255 (2023) [CrossRef] [Google Scholar]
  10. Gupta, K. Gupta, S. Saroha, Short Term Solar Irradiation Forecasting using CEEMDAN Decomposition Based BiLSTM Model Optimized by Genetic Algorithm Approach, IJRED, 11, 736 (2022) [Google Scholar]
  11. T. Perera, C. Udayakumar, Voltage Unbalance Assessment of Solar PV Integrated Low Voltage Distribution System in Sri Lanka Using Monte Carlo Simulation, JSER, 8, 1357 (2023) [Google Scholar]
  12. O. Sharifiyana, M. Dehghani, G. Shahgholian, S.M.M. Mirtalaee, Presenting a New High Gain Boost Converter with Inductive Coupling Energy Recovery Snubber for Renewable Energy Systems- Simulation, Design and Construction, JSER, 8,1417 (2023) [Google Scholar]
  13. H. Zarei Zohdi, M. Sarvi, Optimal and Economic Evaluation of using a Two-axis Solar Tracking System in Photovoltaic Power Plants, a Case Study of “Tehran”, Iran, JSER, 8, 1211 (2023) [Google Scholar]
  14. M. Rajaee, M. Jalali, Construction and Analysis of Smart Solar Bench with the Optimal Angle in Four Central Cities of Iran, JSER, 8, 1345 (2023) [Google Scholar]
  15. Gupta, K. Gupta, and S. Saroha, A review and evaluation of solar forecasting technologies Materials Today: Proceedings, 47, 2420 (2021) [Google Scholar]
  16. Gupta, K. Gupta, and S. Saroha, Solar Irradiation Forecasting Technologies: A Review, SPEE, 30, 319 (2020) [Google Scholar]
  17. P. Singla, M. Duhan, S. Saroha, An ensemble method to forecast 24-h ahead solar irradiance using Wavelet decomposition and BiLSTM deep learning network, ESI, 15, 291 (2022). [Google Scholar]
  18. Gupta, K. Gupta, and S. Saroha, A Review and Evaluation of Solar Forecasting Technologies, MTP, 47, 2420 (2021) [Google Scholar]
  19. Gupta, K. Gupta, S. Saroha, M. Markandeshwar, A 1-hr ahead forecast of solar irradiance using nonlinear auto regressive neural network, Latest Trends in Civil, Mechanical and Electrical Engineering (2021) [Google Scholar]
  20. Gupta, K. Gupta, and S. Saroha, Solar Energy Radiation Forecasting Method, STEES, 105 (2022) [Google Scholar]
  21. https://nsrdb.nrel.gov/data-viewer [Google Scholar]
  22. Gupta, K. Gupta, S. Saroha, A Review of Machine Learning Models in Renewable Energy, ISSCS, 259 (2022) [Google Scholar]
  23. Gupta, K. Gupta, S. Saroha, Single Step-Ahead Solar Irradiation Forecasting Based on Empirical Mode Decomposition with Back Propagation Neural Network, RAPS, 812, 115 (2022) [Google Scholar]
  24. Gupta, K. Gupta, S. Saroha, A Comparative Analysis of Neural Network-Based Models for Forecasting of Solar Irradiation with Different Learning Algorithms, SSEI, 2, 9 (2022) [Google Scholar]
  25. Gupta, K. Gupta, S. Saroha, A New Hybrid Short term solar irradiation forecasting method based on CEEMDAN decomposition approach and BiSLTM deep learning network with grid search algorithm. DGAEJ, 38, 1 (2023) [Google Scholar]
  26. Gupta, K. Gupta, S. Saroha, Single-Step Ahead Solar Irradiance Forecasting Using Hybrid WT-PSO-Based Neural Network, SEAPT, 2, 387 (2022) [Google Scholar]
  27. H. Zang, L. Liu, L. Sun, L. Cheng, Z. Wei, G. Sun, Short-term global horizontal irradiance forecasting based on a hybrid CNNLSTM model with spatiotemporal correlations, RE, 160, 26 (2020) [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.