Open Access
| Issue |
E3S Web Conf.
Volume 257, 2021
5th International Workshop on Advances in Energy Science and Environment Engineering (AESEE 2021)
|
|
|---|---|---|
| Article Number | 03011 | |
| Number of page(s) | 6 | |
| Section | Environmental Monitoring Repair and Pollution Control | |
| DOI | https://doi.org/10.1051/e3sconf/202125703011 | |
| Published online | 12 May 2021 | |
- Duo-sheng, F, Miao, Z, Hui, Z, et al. Solar Power Generation Technology and Application [M]. Beijing: Posts and Telecommunications Press, 2009 [Google Scholar]
- Jure Margeta. Zvonimir Glasnovic .Theoretical settings of photovoltaic-hydro energy system forsustainable energy production[J]. Solar Energy, 2012. [Google Scholar]
- Bacher P, Madsen H, Nielsen H A. Online short-term solar power forecasting[J]. Solar Energy, 2009, 83 (10):1772-1783. [CrossRef] [Google Scholar]
- Yona A, Senjyu T, Saber A Y, et al. Application of neural network to 24-hour-ahead generating powerforecasting for PV system[C]. IEEE Power and Energy Society General Meeting Conversion and Delivery of Electrical Energy in the 21st Century, 2008. [Google Scholar]
- Chang song, C, Shan xu, D, Jin jun, Y. Design of photovoltaic array power generation prediction model based on neural network[J]. Transactions of China Electrotechnical Society, 2009, 24 (9) : 153-158 [Google Scholar]
- Ming, D, Ning zhou, X. Short-term forecasting method of output power of photovoltaic power generation system based on Markov chain Power Network Technology, 2011, 35(1):152-157 [Google Scholar]
- Li bo, W, Zheng ming, Z, Jian zheng, L, et al. Research on Stability of Maximum Power Point Tracking Algorithm for Single-stage Grid-connected PV Inverter System[J]. Proceedings of the CSEE, 2006, 26(6):73-77. [Google Scholar]
- Zheng ming, Z, Yi, L, Fanbo, H, et al. Review of large capacity grid-connected photovoltaic power station technology[J]. Automation of Electric Power Systems, 2011, 35(12):101-107. [Google Scholar]
- Yuan, A, Research on Theory and Method of Water and Light Complementary Coordinated Operation [D]. Xi ’an University of Technology, 2016:53-79 [Google Scholar]
- Lei, W, Research on Short-term Prediction Technology of Output Power of Photovoltaic Power Generation System [D]. Hefei University of Technology, 2012 [Google Scholar]
- Ming, D, Lei, W, Rui, B. A Short-term Prediction Model for Output Power of Photovoltaic Power Generation System Based on Improved BP Neural Network[J]. Power System Protection and Control, 2012, 40(11):93-99. [Google Scholar]
- Lei, W, Research on Short-term Prediction Technology of Output Power of Photovoltaic Power Generation System [D]. Hefei University of Technology, 2012 [Google Scholar]
- Ming, D, Lei, W, Rui, B. A Short-term Prediction Model for Output Power of Photovoltaic Power Generation System Based on Improved BP Neural Network[J]. Power System Protection and Control, 2012, 40(11):93-99. [Google Scholar]
- Fei, W, Zeng qiang, M, Yin xun, Y, et al. Prediction method of photovoltaic power generation based on neural network and associated data[J]. Acta Energiae Solari Sinica, 2012, 33(7): 1171-1177 [Google Scholar]
- Yuan, A, Research on Theory and Method of Water and Light Complementary Coordinated Operation [D]. Xi ’an University of Technology, 2016:53-79 [Google Scholar]
- Mi, Y. Research on key technologies of gridconnection and parallel connection of photovoltaic power generation [D]. Research on key technologies of gridconnection and parallel connection of photovoltaic power generation [D]uazhong University of Science and Technology, 2009. [Google Scholar]
- Guo quan, Q, Yan jun, X, Hong yi, Y. Optimal calculation of solar radiation model for clear days[J]. Acta Energica Sinica, 2001, 22(4):456-460. [Google Scholar]
- Qin man, T, Research on maximum power tracking control method in photovoltaic power generation system [D]. Tianjin University, 2008. [Google Scholar]
- Cheng, C, Reservoir Probabilistic Constraint Scheduling Model and Method Based on Linear Programming [D]. Huazhong University of Science and Technology, 2019:15-69 [Google Scholar]
- Tran H D, Muttil N, Perera B J C. Selection of significant input variables for time series forecasting[J]. Environmental Modeling & Software, 2015, 64:156-163 [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.