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Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting

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  1. Jabar H. Yousif & Hussein A. Kazem & Haitham Al-Balushi & Khaled Abuhmaidan & Reem Al-Badi, 2022. "Artificial Neural Network Modelling and Experimental Evaluation of Dust and Thermal Energy Impact on Monocrystalline and Polycrystalline Photovoltaic Modules," Energies, MDPI, vol. 15(11), pages 1-17, June.
  2. Tang, Huadu & Kang, Fei & Li, Xinyu & Sun, Yong, 2025. "Short-term photovoltaic power prediction model based on feature construction and improved transformer," Energy, Elsevier, vol. 320(C).
  3. Liu, Jingxuan & Zang, Haixiang & Ding, Tao & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2023. "Harvesting spatiotemporal correlation from sky image sequence to improve ultra-short-term solar irradiance forecasting," Renewable Energy, Elsevier, vol. 209(C), pages 619-631.
  4. Wang, Jianzhou & Zhou, Yilin & Li, Zhiwu, 2022. "Hour-ahead photovoltaic generation forecasting method based on machine learning and multi objective optimization algorithm," Applied Energy, Elsevier, vol. 312(C).
  5. Max Olinto Moreira & Betania Mafra Kaizer & Takaaki Ohishi & Benedito Donizeti Bonatto & Antonio Carlos Zambroni de Souza & Pedro Paulo Balestrassi, 2022. "Multivariate Strategy Using Artificial Neural Networks for Seasonal Photovoltaic Generation Forecasting," Energies, MDPI, vol. 16(1), pages 1-30, December.
  6. Iván E. Villalón-Turrubiates & Rogelio López-Herrera & Jorge L. García-Alcaraz & José R. Díaz-Reza & Arturo Soto-Cabral & Iván González-Lazalde & Gerardo Grijalva-Avila & José L. Rodríguez-Álvarez, 2022. "A Non-Invasive Method to Evaluate Fuzzy Process Capability Indices via Coupled Applications of Artificial Neural Networks and the Placket–Burman DOE," Mathematics, MDPI, vol. 10(16), pages 1-27, August.
  7. Yue Guo & Yu Song & Zilong Lai & Xuyang Wang & Licheng Wang & Hui Qin, 2025. "Learning Coupled Meteorological Characteristics Aids Short-Term Photovoltaic Interval Prediction Methods," Energies, MDPI, vol. 18(2), pages 1-17, January.
  8. Yuriy Sayenko & Ryszard Pawelek & Tetiana Baranenko & Vadym Liubartsev, 2025. "The Impact of Meteorological Data on the Accuracy of Solar Electricity Generation Forecasting Using Neural Networks," Energies, MDPI, vol. 18(9), pages 1-19, April.
  9. Cheng, Lilin & Zang, Haixiang & Wei, Zhinong & Zhang, Fengchun & Sun, Guoqiang, 2022. "Evaluation of opaque deep-learning solar power forecast models towards power-grid applications," Renewable Energy, Elsevier, vol. 198(C), pages 960-972.
  10. Pavlos Nikolaidis, 2025. "AI-Enhanced Photovoltaic Power Prediction Under Cross-Continental Dust Events and Air Composition Variability in the Mediterranean Region," Energies, MDPI, vol. 18(14), pages 1-30, July.
  11. Asadi, Meysam & Ramezanzade, Mohsen & Pourhossein, Kazem, 2023. "A global evaluation model applied to wind power plant site selection," Applied Energy, Elsevier, vol. 336(C).
  12. Na Sun & Nan Zhang & Shuai Zhang & Tian Peng & Wei Jiang & Jie Ji & Xiangmiao Hao, 2022. "An Integrated Framework Based on an Improved Gaussian Process Regression and Decomposition Technique for Hourly Solar Radiation Forecasting," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
  13. Lakhdar Nadjib Boucetta & Youssouf Amrane & Aissa Chouder & Saliha Arezki & Sofiane Kichou, 2024. "Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model," Energies, MDPI, vol. 17(7), pages 1-21, April.
  14. Chen, Xiang & Ding, Kun & Zhang, Jingwei & Han, Wei & Liu, Yongjie & Yang, Zenan & Weng, Shuai, 2022. "Online prediction of ultra-short-term photovoltaic power using chaotic characteristic analysis, improved PSO and KELM," Energy, Elsevier, vol. 248(C).
  15. Tahir, Muhammad Faizan & Yousaf, Muhammad Zain & Tzes, Anthony & El Moursi, Mohamed Shawky & El-Fouly, Tarek H.M., 2024. "Enhanced solar photovoltaic power prediction using diverse machine learning algorithms with hyperparameter optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
  16. Zhu, Jianhua & He, Yaoyao, 2025. "A novel hybrid model based on evolving multi-quantile long and short-term memory neural network for ultra-short-term probabilistic forecasting of photovoltaic power," Applied Energy, Elsevier, vol. 377(PC).
  17. Putri Nor Liyana Mohamad Radzi & Muhammad Naveed Akhter & Saad Mekhilef & Noraisyah Mohamed Shah, 2023. "Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
  18. Verdone, Alessio & Panella, Massimo & De Santis, Enrico & Rizzi, Antonello, 2025. "A review of solar and wind energy forecasting: From single-site to multi-site paradigm," Applied Energy, Elsevier, vol. 392(C).
  19. Yu, Min & Niu, Dongxiao & Wang, Keke & Du, Ruoyun & Yu, Xiaoyu & Sun, Lijie & Wang, Feiran, 2023. "Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification," Energy, Elsevier, vol. 275(C).
  20. Niu, Yunbo & Wang, Jianzhou & Zhang, Ziyuan & Luo, Tianrui & Liu, Jingjiang, 2024. "De-Trend First, Attend Next: A Mid-Term PV forecasting system with attention mechanism and encoder–decoder structure," Applied Energy, Elsevier, vol. 353(PB).
  21. 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).
  22. Ma, Huixin & Zhang, Chu & Peng, Tian & Nazir, Muhammad Shahzad & Li, Yiman, 2022. "An integrated framework of gated recurrent unit based on improved sine cosine algorithm for photovoltaic power forecasting," Energy, Elsevier, vol. 256(C).
  23. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
  24. Li, Fengyun & Zheng, Haofeng & Li, Xingmei, 2022. "A novel hybrid model for multi-step ahead photovoltaic power prediction based on conditional time series generative adversarial networks," Renewable Energy, Elsevier, vol. 199(C), pages 560-586.
  25. Ma, Xiangyu & Zhou, Huijie & Li, Zhiyi, 2021. "On the resilience of modern power systems: A complex network perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
  26. Hu, Zehuan & Gao, Yuan & Ji, Siyu & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data," Applied Energy, Elsevier, vol. 359(C).
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