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Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine

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  1. Liu, Xin & Cao, Zheming & Zhang, Zijun, 2021. "Short-term predictions of multiple wind turbine power outputs based on deep neural networks with transfer learning," Energy, Elsevier, vol. 217(C).
  2. Nantian Huang & Enkai Xing & Guowei Cai & Zhiyong Yu & Bin Qi & Lin Lin, 2018. "Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection," Energies, MDPI, vol. 11(7), pages 1-19, June.
  3. Yu, Guangzheng & Liu, Chengquan & Tang, Bo & Chen, Rusi & Lu, Liu & Cui, Chaoyue & Hu, Yue & Shen, Lingxu & Muyeen, S.M., 2022. "Short term wind power prediction for regional wind farms based on spatial-temporal characteristic distribution," Renewable Energy, Elsevier, vol. 199(C), pages 599-612.
  4. Liu, Yuanbin & Hong, Weixiang & Cao, Bingyang, 2019. "Machine learning for predicting thermodynamic properties of pure fluids and their mixtures," Energy, Elsevier, vol. 188(C).
  5. Wang, Xianxun & Virguez, Edgar & Xiao, Weihua & Mei, Yadong & Patiño-Echeverri, Dalia & Wang, Hao, 2019. "Clustering and dispatching hydro, wind, and photovoltaic power resources with multiobjective optimization of power generation fluctuations: A case study in southwestern China," Energy, Elsevier, vol. 189(C).
  6. Fu, Wenlong & Fu, Yuchen & Li, Bailing & Zhang, Hairong & Zhang, Xuanrui & Liu, Jiarui, 2023. "A compound framework incorporating improved outlier detection and correction, VMD, weight-based stacked generalization with enhanced DESMA for multi-step short-term wind speed forecasting," Applied Energy, Elsevier, vol. 348(C).
  7. Xiaodong Yu & Wen Zhang & Hongzhi Zang & Hao Yang, 2018. "Wind Power Interval Forecasting Based on Confidence Interval Optimization," Energies, MDPI, vol. 11(12), pages 1-15, November.
  8. Erhao Meng & Shengzhi Huang & Qiang Huang & Wei Fang & Hao Wang & Guoyong Leng & Lu Wang & Hao Liang, 2021. "A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1321-1337, March.
  9. Fu, Wenlong & Fang, Ping & Wang, Kai & Li, Zhenxing & Xiong, Dongzhen & Zhang, Kai, 2021. "Multi-step ahead short-term wind speed forecasting approach coupling variational mode decomposition, improved beetle antennae search algorithm-based synchronous optimization and Volterra series model," Renewable Energy, Elsevier, vol. 179(C), pages 1122-1139.
  10. Wang, Bingqing & Li, Yongping & Huang, Guohe & Gao, Pangpang & Liu, Jing & Wen, Yizhuo, 2023. "Development of an integrated BLSVM-MFA method for analyzing renewable power-generation potential under climate change: A case study of Xiamen," Applied Energy, Elsevier, vol. 337(C).
  11. Meihang Zhang & Hua Zhang & Wei Yan & Zhigang Jiang & Shuo Zhu, 2023. "An Integrated Deep-Learning-Based Approach for Energy Consumption Prediction of Machining Systems," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
  12. Abbas, Ahmed K. & Bashikh, Ali A. & Abbas, Hayder & Mohammed, Haider Q., 2019. "Intelligent decisions to stop or mitigate lost circulation based on machine learning," Energy, Elsevier, vol. 183(C), pages 1104-1113.
  13. García-Nieto, P.J. & García-Gonzalo, E. & Fernández, J.R. Alonso & Muñiz, C. Díaz, 2019. "Modeling of the algal atypical increase in La Barca reservoir using the DE optimized least square support vector machine approach with feature selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 166(C), pages 461-480.
  14. Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.
  15. Zhiqiang Jiang & Peibing Song & Xiang Liao, 2020. "Optimization of Year-End Water Level of Multi-Year Regulating Reservoir in Cascade Hydropower System Considering the Inflow Frequency Difference," Energies, MDPI, vol. 13(20), pages 1-20, October.
  16. Hu, Huanling & Wang, Lin & Lv, Sheng-Xiang, 2020. "Forecasting energy consumption and wind power generation using deep echo state network," Renewable Energy, Elsevier, vol. 154(C), pages 598-613.
  17. Shengli Liao & Xudong Tian & Benxi Liu & Tian Liu & Huaying Su & Binbin Zhou, 2022. "Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis," Energies, MDPI, vol. 15(17), pages 1-21, August.
  18. Vadim Manusov & Pavel Matrenin & Muso Nazarov & Svetlana Beryozkina & Murodbek Safaraliev & Inga Zicmane & Anvari Ghulomzoda, 2023. "Short-Term Prediction of the Wind Speed Based on a Learning Process Control Algorithm in Isolated Power Systems," Sustainability, MDPI, vol. 15(2), pages 1-12, January.
  19. 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.
  20. Madeline Hui Li Lee & Yee Chee Ser & Ganeshsree Selvachandran & Pham Huy Thong & Le Cuong & Le Hoang Son & Nguyen Trung Tuan & Vassilis C. Gerogiannis, 2022. "A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models," Mathematics, MDPI, vol. 10(8), pages 1-23, April.
  21. Al-qaness, Mohammed A.A. & Ewees, Ahmed A. & Fan, Hong & Abualigah, Laith & Elaziz, Mohamed Abd, 2022. "Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting," Applied Energy, Elsevier, vol. 314(C).
  22. Farah, Shahid & David A, Wood & Humaira, Nisar & Aneela, Zameer & Steffen, Eger, 2022. "Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
  23. Manisha Sawant & Rupali Patil & Tanmay Shikhare & Shreyas Nagle & Sakshi Chavan & Shivang Negi & Neeraj Dhanraj Bokde, 2022. "A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction," Energies, MDPI, vol. 15(21), pages 1-24, October.
  24. Zhang, Zhendong & Ye, Lei & Qin, Hui & Liu, Yongqi & Wang, Chao & Yu, Xiang & Yin, Xingli & Li, Jie, 2019. "Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression," Applied Energy, Elsevier, vol. 247(C), pages 270-284.
  25. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.
  26. Wang, Jujie & Li, Yaning, 2018. "Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy," Applied Energy, Elsevier, vol. 230(C), pages 429-443.
  27. Long, Huan & Zhang, Zijun & Sun, Mu-Xia & Li, Yan-Fu, 2018. "The data-driven schedule of wind farm power generations and required reserves," Energy, Elsevier, vol. 149(C), pages 485-495.
  28. Hu, Shuai & Xiang, Yue & Huo, Da & Jawad, Shafqat & Liu, Junyong, 2021. "An improved deep belief network based hybrid forecasting method for wind power," Energy, Elsevier, vol. 224(C).
  29. Neshat, Mehdi & Nezhad, Meysam Majidi & Abbasnejad, Ehsan & Mirjalili, Seyedali & Groppi, Daniele & Heydari, Azim & Tjernberg, Lina Bertling & Astiaso Garcia, Davide & Alexander, Bradley & Shi, Qinfen, 2021. "Wind turbine power output prediction using a new hybrid neuro-evolutionary method," Energy, Elsevier, vol. 229(C).
  30. Zhang, Binqiao & Yuan, Xiaohui & Yuan, Yanbin & Wang, Xu, 2018. "An active perceivable device–oriented modeling framework for hydropower plant simulation," Energy, Elsevier, vol. 165(PB), pages 1009-1023.
  31. Chen, Zhihuan & Yuan, Xiaohui & Yuan, Yanbin & Lei, Xiaohui & Zhang, Binqiao, 2019. "Parameter estimation of fuzzy sliding mode controller for hydraulic turbine regulating system based on HICA algorithm," Renewable Energy, Elsevier, vol. 133(C), pages 551-565.
  32. Xiaoshuang Huang & Yinbao Zhang & Jianzhong Liu & Xinjia Zhang & Sicong Liu, 2023. "A Short-Term Wind Power Forecasting Model Based on 3D Convolutional Neural Network–Gated Recurrent Unit," Sustainability, MDPI, vol. 15(19), pages 1-13, September.
  33. Xing Zhang, 2018. "Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm," Energies, MDPI, vol. 11(6), pages 1-18, June.
  34. Fu, Wenlong & Zhang, Kai & Wang, Kai & Wen, Bin & Fang, Ping & Zou, Feng, 2021. "A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM," Renewable Energy, Elsevier, vol. 164(C), pages 211-229.
  35. Zhang, Yachao & Le, Jian & Liao, Xiaobing & Zheng, Feng & Li, Yinghai, 2019. "A novel combination forecasting model for wind power integrating least square support vector machine, deep belief network, singular spectrum analysis and locality-sensitive hashing," Energy, Elsevier, vol. 168(C), pages 558-572.
  36. Feng Xing & Xiaoyu Song & Yubo Wang & Caiyan Qin, 2023. "A New Combined Prediction Model for Ultra-Short-Term Wind Power Based on Variational Mode Decomposition and Gradient Boosting Regression Tree," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
  37. 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).
  38. Tingkai Gong & Xiaohui Yuan & Xu Wang & Yanbin Yuan & Binqiao Zhang, 2020. "Fault diagnosis for rolling element bearing using variational mode decomposition and l1 trend filtering," Journal of Risk and Reliability, , vol. 234(1), pages 116-128, February.
  39. Xu, Xuefang & Hu, Shiting & Shi, Peiming & Shao, Huaishuang & Li, Ruixiong & Li, Zhi, 2023. "Natural phase space reconstruction-based broad learning system for short-term wind speed prediction: Case studies of an offshore wind farm," Energy, Elsevier, vol. 262(PA).
  40. Xiaoyu Shi & Xuewen Lei & Qiang Huang & Shengzhi Huang & Kun Ren & Yuanyuan Hu, 2018. "Hourly Day-Ahead Wind Power Prediction Using the Hybrid Model of Variational Model Decomposition and Long Short-Term Memory," Energies, MDPI, vol. 11(11), pages 1-20, November.
  41. Wang, Jianzhou & Yang, Wendong & Du, Pei & Li, Yifan, 2018. "Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system," Energy, Elsevier, vol. 148(C), pages 59-78.
  42. Mohammad Rezaie-Balf & Niloofar Maleki & Sungwon Kim & Ali Ashrafian & Fatemeh Babaie-Miri & Nam Won Kim & Il-Moon Chung & Sina Alaghmand, 2019. "Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm," Energies, MDPI, vol. 12(8), pages 1-23, April.
  43. Zheng, Ling & Zhou, Bin & Or, Siu Wing & Cao, Yijia & Wang, Huaizhi & Li, Yong & Chan, Ka Wing, 2021. "Spatio-temporal wind speed prediction of multiple wind farms using capsule network," Renewable Energy, Elsevier, vol. 175(C), pages 718-730.
  44. Bai, Yulong & Liu, Ming-De & Ding, Lin & Ma, Yong-Jie, 2021. "Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition," Applied Energy, Elsevier, vol. 301(C).
  45. López, Germánico & Arboleya, Pablo, 2022. "Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes Mountains, Ecuador," Renewable Energy, Elsevier, vol. 183(C), pages 351-368.
  46. Wu, Qiang & Zheng, Hongling & Guo, Xiaozhu & Liu, Guangqiang, 2022. "Promoting wind energy for sustainable development by precise wind speed prediction based on graph neural networks," Renewable Energy, Elsevier, vol. 199(C), pages 977-992.
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