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A novel application of an analog ensemble for short-term wind power forecasting

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  1. Cheng, William Y.Y. & Liu, Yubao & Bourgeois, Alfred J. & Wu, Yonghui & Haupt, Sue Ellen, 2017. "Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation," Renewable Energy, Elsevier, vol. 107(C), pages 340-351.
  2. Gallego-Castillo, Cristobal & Bessa, Ricardo & Cavalcante, Laura & Lopez-Garcia, Oscar, 2016. "On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power," Energy, Elsevier, vol. 113(C), pages 355-365.
  3. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
  4. Lahouar, A. & Ben Hadj Slama, J., 2017. "Hour-ahead wind power forecast based on random forests," Renewable Energy, Elsevier, vol. 109(C), pages 529-541.
  5. Cervone, Guido & Clemente-Harding, Laura & Alessandrini, Stefano & Delle Monache, Luca, 2017. "Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble," Renewable Energy, Elsevier, vol. 108(C), pages 274-286.
  6. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
  7. Stefano Alessandrini & Tyler McCandless, 2020. "The Schaake Shuffle Technique to Combine Solar and Wind Power Probabilistic Forecasting," Energies, MDPI, vol. 13(10), pages 1-18, May.
  8. Branko Kosovic & Sue Ellen Haupt & Daniel Adriaansen & Stefano Alessandrini & Gerry Wiener & Luca Delle Monache & Yubao Liu & Seth Linden & Tara Jensen & William Cheng & Marcia Politovich & Paul Prest, 2020. "A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction," Energies, MDPI, vol. 13(6), pages 1-16, March.
  9. Monforti, Fabio & Gonzalez-Aparicio, Iratxe, 2017. "Comparing the impact of uncertainties on technical and meteorological parameters in wind power time series modelling in the European Union," Applied Energy, Elsevier, vol. 206(C), pages 439-450.
  10. 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.
  11. Pappa, Areti & Theodoropoulos, Ioannis & Galmarini, Stefano & Kioutsioukis, Ioannis, 2023. "Analog versus multi-model ensemble forecasting: A comparison for renewable energy resources," Renewable Energy, Elsevier, vol. 205(C), pages 563-573.
  12. Yiqi Chu & Chengcai Li & Yefang Wang & Jing Li & Jian Li, 2016. "A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction," Energies, MDPI, vol. 9(11), pages 1-20, October.
  13. Dongxiao Niu & Di Pu & Shuyu Dai, 2018. "Ultra-Short-Term Wind-Power Forecasting Based on the Weighted Random Forest Optimized by the Niche Immune Lion Algorithm," Energies, MDPI, vol. 11(5), pages 1-21, April.
  14. Jannik Schütz Roungkvist & Peter Enevoldsen, 2020. "Timescale classification in wind forecasting: A review of the state‐of‐the‐art," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 757-768, August.
  15. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.
  16. Salcedo-Sanz, S. & García-Herrera, R. & Camacho-Gómez, C. & Aybar-Ruíz, A. & Alexandre, E., 2018. "Wind power field reconstruction from a reduced set of representative measuring points," Applied Energy, Elsevier, vol. 228(C), pages 1111-1121.
  17. Marino Marrocu & Luca Massidda, 2017. "A Simple and Effective Approach for the Prediction of Turbine Power Production From Wind Speed Forecast," Energies, MDPI, vol. 10(12), pages 1-14, November.
  18. González-Aparicio, I. & Zucker, A., 2015. "Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain," Applied Energy, Elsevier, vol. 159(C), pages 334-349.
  19. Hao, Ying & Dong, Lei & Liao, Xiaozhong & Liang, Jun & Wang, Lijie & Wang, Bo, 2019. "A novel clustering algorithm based on mathematical morphology for wind power generation prediction," Renewable Energy, Elsevier, vol. 136(C), pages 572-585.
  20. Shahriari, M. & Cervone, G. & Clemente-Harding, L. & Delle Monache, L., 2020. "Using the analog ensemble method as a proxy measurement for wind power predictability," Renewable Energy, Elsevier, vol. 146(C), pages 789-801.
  21. Hocaoglu, Fatih Onur & Serttas, Fatih, 2017. "A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 108(C), pages 635-643.
  22. Costa, Marcelo Azevedo & Ruiz-Cárdenas, Ramiro & Mineti, Leandro Brioschi & Prates, Marcos Oliveira, 2021. "Dynamic time scan forecasting for multi-step wind speed prediction," Renewable Energy, Elsevier, vol. 177(C), pages 584-595.
  23. Wang, Cong & Zhang, Hongli & Fan, Wenhui & Ma, Ping, 2017. "A new chaotic time series hybrid prediction method of wind power based on EEMD-SE and full-parameters continued fraction," Energy, Elsevier, vol. 138(C), pages 977-990.
  24. Nakıp, Mert & Çopur, Onur & Biyik, Emrah & Güzeliş, Cüneyt, 2023. "Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network," Applied Energy, Elsevier, vol. 340(C).
  25. Ricardo Torres-López & David Casillas-Pérez & Jorge Pérez-Aracil & Laura Cornejo-Bueno & Enrique Alexandre & Sancho Salcedo-Sanz, 2022. "Analysis of Machine Learning Approaches’ Performance in Prediction Problems with Human Activity Patterns," Mathematics, MDPI, vol. 10(13), pages 1-18, June.
  26. Song, Xiaodong & Johnson, Paul & Duck, Peter, 2021. "A novel combination of Mycielski–Markov, regime switching and jump diffusion models for solar energy," Applied Energy, Elsevier, vol. 301(C).
  27. González-Sopeña, J.M. & Pakrashi, V. & Ghosh, B., 2021. "An overview of performance evaluation metrics for short-term statistical wind power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
  28. Fatemi, Seyyed A. & Kuh, Anthony & Fripp, Matthias, 2018. "Parametric methods for probabilistic forecasting of solar irradiance," Renewable Energy, Elsevier, vol. 129(PA), pages 666-676.
  29. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
  30. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Cervone, G., 2015. "An analog ensemble for short-term probabilistic solar power forecast," Applied Energy, Elsevier, vol. 157(C), pages 95-110.
  31. Sue Ellen Haupt & Tyler C. McCandless & Susan Dettling & Stefano Alessandrini & Jared A. Lee & Seth Linden & William Petzke & Thomas Brummet & Nhi Nguyen & Branko Kosović & Gerry Wiener & Tahani Hussa, 2020. "Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting," Energies, MDPI, vol. 13(8), pages 1-23, April.
  32. Shuxia Yang & Xianguo Zhu & Shengjiang Peng, 2020. "Prospect Prediction of Terminal Clean Power Consumption in China via LSSVM Algorithm Based on Improved Evolutionary Game Theory," Energies, MDPI, vol. 13(8), pages 1-17, April.
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