IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v247y2019icp389-402.html
   My bibliography  Save this item

Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Llinet Benavides Cesar & Rodrigo Amaro e Silva & Miguel Ángel Manso Callejo & Calimanut-Ionut Cira, 2022. "Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates," Energies, MDPI, vol. 15(12), pages 1-23, June.
  2. Yin, He & Yang, Mao-sen & Lan, Hai & Hong, Ying-Yi & Guo, Dong & Jin, Feng, 2024. "A hybrid graph attention network based method for interval prediction of shipboard solar irradiation," Energy, Elsevier, vol. 298(C).
  3. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Zhang, Jun & Shi, Junsheng & Gao, Bixuan & Liu, Wuming, 2021. "Hybrid deep neural model for hourly solar irradiance forecasting," Renewable Energy, Elsevier, vol. 171(C), pages 1041-1060.
  4. Brahim Belmahdi & Mohamed Louzazni & Mousa Marzband & Abdelmajid El Bouardi, 2023. "Global Solar Radiation Forecasting Based on Hybrid Model with Combinations of Meteorological Parameters: Morocco Case Study," Forecasting, MDPI, vol. 5(1), pages 1-24, January.
  5. Julián Urrego-Ortiz & J. Alejandro Martínez & Paola A. Arias & Álvaro Jaramillo-Duque, 2019. "Assessment and Day-Ahead Forecasting of Hourly Solar Radiation in Medellín, Colombia," Energies, MDPI, vol. 12(22), pages 1-29, November.
  6. Martins, Guilherme Santos & Giesbrecht, Mateus, 2023. "Hybrid approaches based on Singular Spectrum Analysis and k- Nearest Neighbors for clearness index forecasting," Renewable Energy, Elsevier, vol. 219(P1).
  7. Severiano, Carlos A. & Silva, Petrônio Cândido de Lima e & Weiss Cohen, Miri & Guimarães, Frederico Gadelha, 2021. "Evolving fuzzy time series for spatio-temporal forecasting in renewable energy systems," Renewable Energy, Elsevier, vol. 171(C), pages 764-783.
  8. Gao, Bixuan & Huang, Xiaoqiao & Shi, Junsheng & Tai, Yonghang & Zhang, Jun, 2020. "Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 1665-1683.
  9. Wei, Ziqing & Zhang, Tingwei & Yue, Bao & Ding, Yunxiao & Xiao, Ran & Wang, Ruzhu & Zhai, Xiaoqiang, 2021. "Prediction of residential district heating load based on machine learning: A case study," Energy, Elsevier, vol. 231(C).
  10. Guijo-Rubio, D. & Durán-Rosal, A.M. & Gutiérrez, P.A. & Gómez-Orellana, A.M. & Casanova-Mateo, C. & Sanz-Justo, J. & Salcedo-Sanz, S. & Hervás-Martínez, C., 2020. "Evolutionary artificial neural networks for accurate solar radiation prediction," Energy, Elsevier, vol. 210(C).
  11. Xinyu Yang & Ying Ji & Xiaoxia Wang & Menghan Niu & Shuijing Long & Jingchao Xie & Yuying Sun, 2023. "Simplified Method for Predicting Hourly Global Solar Radiation Using Extraterrestrial Radiation and Limited Weather Forecast Parameters," Energies, MDPI, vol. 16(7), pages 1-16, April.
  12. Lei Fu & Yiling Yang & Xiaolong Yao & Xufen Jiao & Tiantian Zhu, 2019. "A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion," Energies, MDPI, vol. 12(20), pages 1-23, October.
  13. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
  14. Ji, Ying & Chen, Xiang & Yang, Xinyu & Wang, Xinyue & Wang, Xiaoxia & Xie, Jingchao & Ju, Guidong, 2024. "Research on the framework and meteorological parameter optimization method of dynamic heating load prediction model for heat-exchange stations," Energy, Elsevier, vol. 309(C).
  15. Jamali Jahromi, Ali & Mohammadi, Mohammad & Afrasiabi, Shahabodin & Afrasiabi, Mousa & Aghaei, Jamshid, 2022. "Probability density function forecasting of residential electric vehicles charging profile," Applied Energy, Elsevier, vol. 323(C).
  16. Yuan-Kang Wu & Cheng-Liang Huang & Quoc-Thang Phan & Yuan-Yao Li, 2022. "Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints," Energies, MDPI, vol. 15(9), pages 1-22, May.
  17. Shi, Jihao & Li, Junjie & Usmani, Asif Sohail & Zhu, Yuan & Chen, Guoming & Yang, Dongdong, 2021. "Probabilistic real-time deep-water natural gas hydrate dispersion modeling by using a novel hybrid deep learning approach," Energy, Elsevier, vol. 219(C).
  18. Xie, Tuo & Yun, Xinyao & Zhang, Gang & Li, Hua & Zhang, Kaoshe & Wang, Ruogu, 2024. "Charging station cluster load prediction: Spatiotemporal multi-graph fusion technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 206(C).
  19. Zhou, Yi & Zhou, Nanrun & Gong, Lihua & Jiang, Minlin, 2020. "Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine," Energy, Elsevier, vol. 204(C).
  20. Zhao, Wei & Zhang, Haoran & Zheng, Jianqin & Dai, Yuanhao & Huang, Liqiao & Shang, Wenlong & Liang, Yongtu, 2021. "A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants," Energy, Elsevier, vol. 223(C).
  21. Konstantinos Blazakis & Yiannis Katsigiannis & Georgios Stavrakakis, 2022. "One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques," Energies, MDPI, vol. 15(12), pages 1-25, June.
  22. Gupta, Priya & Singh, Rhythm, 2023. "Combining a deep learning model with multivariate empirical mode decomposition for hourly global horizontal irradiance forecasting," Renewable Energy, Elsevier, vol. 206(C), pages 908-927.
  23. Zheng, Jianqin & Zhang, Haoran & Dai, Yuanhao & Wang, Bohong & Zheng, Taicheng & Liao, Qi & Liang, Yongtu & Zhang, Fengwei & Song, Xuan, 2020. "Time series prediction for output of multi-region solar power plants," Applied Energy, Elsevier, vol. 257(C).
  24. Armando Castillejo-Cuberos & John Boland & Rodrigo Escobar, 2021. "Short-Term Deterministic Solar Irradiance Forecasting Considering a Heuristics-Based, Operational Approach," Energies, MDPI, vol. 14(18), pages 1-24, September.
  25. Fatemeh Ghobadi & Amir Saman Tayerani Charmchi & Doosun Kang, 2023. "Feature Extraction from Satellite-Derived Hydroclimate Data: Assessing Impacts on Various Neural Networks for Multi-Step Ahead Streamflow Prediction," Sustainability, MDPI, vol. 15(22), pages 1-32, November.
  26. Teng, Sin Yong & Máša, Vítězslav & Touš, Michal & Vondra, Marek & Lam, Hon Loong & Stehlík, Petr, 2022. "Waste-to-energy forecasting and real-time optimization: An anomaly-aware approach," Renewable Energy, Elsevier, vol. 181(C), pages 142-155.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.