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Random forest solar power forecast based on classification optimization

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Cited by:

  1. Li, Qing & Zhang, Xinyan & Ma, Tianjiao & Jiao, Chunlei & Wang, Heng & Hu, Wei, 2021. "A multi-step ahead photovoltaic power prediction model based on similar day, enhanced colliding bodies optimization, variational mode decomposition, and deep extreme learning machine," Energy, Elsevier, vol. 224(C).
  2. Korkmaz, Deniz, 2021. "SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 300(C).
  3. Khalilnejad, Arash & French, Roger H. & Abramson, Alexis R., 2021. "Evaluation of cooling setpoint setback savings in commercial buildings using electricity and exterior temperature time series data," Energy, Elsevier, vol. 233(C).
  4. Jebli, Imane & Belouadha, Fatima-Zahra & Kabbaj, Mohammed Issam & Tilioua, Amine, 2021. "Prediction of solar energy guided by pearson correlation using machine learning," Energy, Elsevier, vol. 224(C).
  5. Nguyen, Thi Ngoc & Müsgens, Felix, 2022. "What drives the accuracy of PV output forecasts?," Applied Energy, Elsevier, vol. 323(C).
  6. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
  7. Sibtain, Muhammad & Li, Xianshan & Saleem, Snoober & Ain, Qurat-ul- & Shi, Qiang & Li, Fei & Saeed, Muhammad & Majeed, Fatima & Shah, Syed Shoaib Ahmed & Saeed, Muhammad Hammad, 2022. "Multifaceted irradiance prediction by exploiting hybrid decomposition-entropy-Spatiotemporal attention based Sequence2Sequence models," Renewable Energy, Elsevier, vol. 196(C), pages 648-682.
  8. Thi Ngoc Nguyen & Felix Musgens, 2021. "What drives the accuracy of PV output forecasts?," Papers 2111.02092, arXiv.org.
  9. Scott, Connor & Ahsan, Mominul & Albarbar, Alhussein, 2023. "Machine learning for forecasting a photovoltaic (PV) generation system," Energy, Elsevier, vol. 278(C).
  10. Carneiro, Tatiane C. & Rocha, Paulo A.C. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M., 2022. "Ridge regression ensemble of machine learning models applied to solar and wind forecasting in Brazil and Spain," Applied Energy, Elsevier, vol. 314(C).
  11. Alipour, Mohammadali & Aghaei, Jamshid & Norouzi, Mohammadali & Niknam, Taher & Hashemi, Sattar & Lehtonen, Matti, 2020. "A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration," Energy, Elsevier, vol. 205(C).
  12. 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.
  13. Jieyun Zheng & Linyao Zhang & Jinpeng Chen & Guilian Wu & Shiyuan Ni & Zhijian Hu & Changhong Weng & Zhi Chen, 2021. "Multiple-Load Forecasting for Integrated Energy System Based on Copula-DBiLSTM," Energies, MDPI, vol. 14(8), pages 1-14, April.
  14. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
  15. Abdallah Abdellatif & Hamza Mubarak & Shameem Ahmad & Tofael Ahmed & G. M. Shafiullah & Ahmad Hammoudeh & Hamdan Abdellatef & M. M. Rahman & Hassan Muwafaq Gheni, 2022. "Forecasting Photovoltaic Power Generation with a Stacking Ensemble Model," Sustainability, MDPI, vol. 14(17), pages 1-21, September.
  16. Jesús Polo & Nuria Martín-Chivelet & Miguel Alonso-Abella & Carlos Sanz-Saiz & José Cuenca & Marina de la Cruz, 2023. "Exploring the PV Power Forecasting at Building Façades Using Gradient Boosting Methods," Energies, MDPI, vol. 16(3), pages 1-12, February.
  17. Baoyong Yan & Xiantao Zhang & Chengxu Tang & Xiao Wang & Yifei Yang & Weihua Xu, 2023. "A Random Forest-Based Method for Predicting Borehole Trajectories," Mathematics, MDPI, vol. 11(6), pages 1-15, March.
  18. Li, Chengdong & Zhou, Changgeng & Peng, Wei & Lv, Yisheng & Luo, Xin, 2020. "Accurate prediction of short-term photovoltaic power generation via a novel double-input-rule-modules stacked deep fuzzy method," Energy, Elsevier, vol. 212(C).
  19. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).
  20. 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).
  21. Andi A. H. Lateko & Hong-Tzer Yang & Chao-Ming Huang, 2022. "Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method," Energies, MDPI, vol. 15(11), pages 1-21, June.
  22. 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.
  23. 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).
  24. Junfeng Kang & Xinyi Zou & Jianlin Tan & Jun Li & Hamed Karimian, 2023. "Short-Term PM 2.5 Concentration Changes Prediction: A Comparison of Meteorological and Historical Data," Sustainability, MDPI, vol. 15(14), pages 1-24, July.
  25. Li, Yanbin & Zhao, Ke & Zhang, Feng, 2023. "Identification of key influencing factors to Chinese coal power enterprises transition in the context of carbon neutrality: A modified fuzzy DEMATEL approach," Energy, Elsevier, vol. 263(PA).
  26. Kim, Jimin & Obregon, Josue & Park, Hoonseok & Jung, Jae-Yoon, 2024. "Multi-step photovoltaic power forecasting using transformer and recurrent neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
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