Open Access
Issue |
E3S Web Conf.
Volume 423, 2023
2023 7th International Workshop on Renewable Energy and Development (IWRED 2023)
|
|
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Article Number | 01009 | |
Number of page(s) | 7 | |
Section | Biomass Energy Conversion and Power Generation System Application | |
DOI | https://doi.org/10.1051/e3sconf/202342301009 | |
Published online | 08 September 2023 |
- Wang Changqing. Discussion on the technical characteristics and applications of solar thermal power generation[J]. China market, 2019, 25(34):8890. DOI: 10.13939/j.cnki.zgsc.2019.34.088 [Google Scholar]
- Li Zhonghong, He Lesheng, Wang Jing, et al. Research on multi-scale photovoltaic power prediction based on full mining of meteorological information[J]. China Measurement & Test, 2022, 48(12):111117. https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7ioT0BO4yQ4m_mOgeS2ml3UDJaGIDTe3tC71O4xwaZkGMSpdmhWCVuoBKR9IMSpta&uniplatform=NZKPT [Google Scholar]
- Ding Ming, Wang Lei, Bi Rui. A short-term prediction model to forecast output power of photovoltaic system based on improved BP neural network[J]. Power System Protection and Control, 2012, 40(11):9399. https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKgchrJ08w1e7fm4X_1ttJAkXBqTt_GkFylWsjGWC0bRipwxUOZKCtjun5KouCTomKWAjrDHCPB13&uniplatform=NZKPT [Google Scholar]
- Deng Weisi, Meng Zichao, Wang Haohuai, et al. Renewable energy power prediction characteristics analyses and accuracy improvementmeasures[J]. Southern Power System Technology, 2023, 17(2):1123. DOI: 10.13648/j.cnki.issn1674-0629.2023.02.003 [Google Scholar]
- Chen Changsong, Duan Shanxu, Yin Jinjun. Design of photovoltaic array power generation prediction model based on neural network[J]. Transactions of China ElectrotechnicalSociety, 2009, 24(9):153-158. DOI: 10.19595/j.cnki.10006753.tces.2009.09.023 [Google Scholar]
- Zhu Linlin, Zhong Zhifeng, Yan Hai, et al. A new design of photovoltaic power generation forecasting mo -del[J]. Acta Energiae Solaris Sinica, 2016, 37(1): 63-68. https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7ijP0rjQDAVm8oHBO0FTadhMtfHRhnUwfE8XAorofacVtGflHSpAsyVZdO8Yr4GjY&uniplatform=NZKPT [Google Scholar]
- Ding Ming, Wang Lei, Bi Rui. A short-term prediction model to forecast output power of photovoltaic system based on improved BP neural network[J]. Power System Protection and Control, 2012, 40(11):9399. https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKgchrJ08w1e7fm4X_1ttJAkXBqTt_GkFylWsjGWC0bRipwxUOZKCtjun5KouCTomKWAjrDHCPB13&uniplatform=NZKPT [Google Scholar]
- He Dong, Liu Ruiye. Ultra-short-term wind power prediction using ANN ensemble based on the principal components analysis[J]. Power System Protection and Control, 2013, 41(4): 5054. https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKgchrJ08w1e7xAZywCwkEEIyfIbD7Iew_BBuJY5Qjq9dIhNCt0GeiCganc4hmRpLugQbhLO1nbuN&uniplatform=NZKPT [Google Scholar]
- Wang Xinpu, Zhou Xiangling, Xing Jie, et al. Prediction method of PV output power based on the combination of improved grey back propagation neural network[J]. Power System Protection and Control, 2016, 44(18): 81-87. https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7ijP0rjQDAVm8oHBO0FTadpcWfbz2WCB7BT0cCxLvGdfNcFdho2zcQlfk7xJNsf4b&uniplatform=NZKPT [Google Scholar]
- Wei X H. Short term photovoltaic power prediction based on grey model and machine learning[D]. Lanzhou : Lanzhou University, 2019. https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C475KOm_zrgu4lQARvep2SAkOsSuGHvNoCRcTRpJSuXuqe2BzK-HlJJFZOgmRRVlsMFQatP7gS4iGcvmSXlVZ9E&uniplatform=NZKPT [Google Scholar]
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