Online prediction of ultra-short-term photovoltaic power using chaotic characteristic analysis, improved PSO and KELM
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DOI: 10.1016/j.energy.2022.123574
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- Wenlong Fu & Kai Wang & Jianzhong Zhou & Yanhe Xu & Jiawen Tan & Tie Chen, 2019. "A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy," Sustainability, MDPI, vol. 11(6), pages 1-24, March.
- Zhen, Hao & Niu, Dongxiao & Wang, Keke & Shi, Yucheng & Ji, Zhengsen & Xu, Xiaomin, 2021. "Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information," Energy, Elsevier, vol. 231(C).
- Zhenyu Wang & Cuixia Tian & Qibing Zhu & Min Huang, 2018. "Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition," Energies, MDPI, vol. 11(1), pages 1-21, January.
- Eseye, Abinet Tesfaye & Zhang, Jianhua & Zheng, Dehua, 2018. "Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information," Renewable Energy, Elsevier, vol. 118(C), pages 357-367.
- Han, Li & Romero, Carlos E. & Yao, Zheng, 2015. "Wind power forecasting based on principle component phase space reconstruction," Renewable Energy, Elsevier, vol. 81(C), pages 737-744.
- Niya Chen & Zheng Qian & Xiaofeng Meng, 2013. "Multistep Wind Speed Forecasting Based on Wavelet and Gaussian Processes," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-8, August.
- Moreira, M.O. & Balestrassi, P.P. & Paiva, A.P. & Ribeiro, P.F. & Bonatto, B.D., 2021. "Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
- Fei Mei & Yi Pan & Kedong Zhu & Jianyong Zheng, 2018. "A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation," Sustainability, MDPI, vol. 10(3), pages 1-17, March.
- Li, Pengtao & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, Elsevier, vol. 259(C).
- Gu, Bo & Shen, Huiqiang & Lei, Xiaohui & Hu, Hao & Liu, Xinyu, 2021. "Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method," Applied Energy, Elsevier, vol. 299(C).
- Moslehi, Salim & Reddy, T. Agami & Katipamula, Srinivas, 2018. "Evaluation of data-driven models for predicting solar photovoltaics power output," Energy, Elsevier, vol. 142(C), pages 1057-1065.
- Bemani, Amin & Xiong, Qingang & Baghban, Alireza & Habibzadeh, Sajjad & Mohammadi, Amir H. & Doranehgard, Mohammad Hossein, 2020. "Modeling of cetane number of biodiesel from fatty acid methyl ester (FAME) information using GA-, PSO-, and HGAPSO- LSSVM models," Renewable Energy, Elsevier, vol. 150(C), pages 924-934.
- Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
- Yufei Wang & Li Zhu & Hua Xue, 2020. "Ultra-Short-Term Photovoltaic Power Prediction Model Based on the Localized Emotion Reconstruction Emotional Neural Network," Energies, MDPI, vol. 13(11), pages 1-21, June.
- Sharadga, Hussein & Hajimirza, Shima & Balog, Robert S., 2020. "Time series forecasting of solar power generation for large-scale photovoltaic plants," Renewable Energy, Elsevier, vol. 150(C), pages 797-807.
- De Giorgi, M.G. & Malvoni, M. & Congedo, P.M., 2016. "Comparison of strategies for multi-step ahead photovoltaic power forecasting models based on hybrid group method of data handling networks and least square support vector machine," Energy, Elsevier, vol. 107(C), pages 360-373.
- Wang, Jian-Zhou & Wang, Yun & Jiang, Ping, 2015. "The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China," Applied Energy, Elsevier, vol. 143(C), pages 472-488.
- Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
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Cited by:
- Wentao Ma & Lihong Qiu & Fengyuan Sun & Sherif S. M. Ghoneim & Jiandong Duan, 2022. "PV Power Forecasting Based on Relevance Vector Machine with Sparrow Search Algorithm Considering Seasonal Distribution and Weather Type," Energies, MDPI, vol. 15(14), pages 1-24, July.
- Chen, Xiang & Ding, Kun & Yang, Hang & Chen, Xihui & Zhang, Jingwei & Jiang, Meng & Gao, Ruiguang & Liu, Zengquan, 2023. "Research on real-time identification method of model parameters for the photovoltaic array," Applied Energy, Elsevier, vol. 342(C).
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Keywords
Photovoltaic power; Chaotic characteristic analysis; Data pre-processing; Parameters extraction; Online prediction;All these keywords.
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