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A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting

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Cited by:

  1. Jiu Gu & Yining Wang & Da Xie & Yu Zhang, 2019. "Wind Farm NWP Data Preprocessing Method Based on t-SNE," Energies, MDPI, vol. 12(19), pages 1-16, September.
  2. Ru Hou & Yi Yang & Qingcong Yuan & Yanhua Chen, 2019. "Research and Application of Hybrid Wind-Energy Forecasting Models Based on Cuckoo Search Optimization," Energies, MDPI, vol. 12(19), pages 1-17, September.
  3. Tian, Zhirui & Wang, Jiyang, 2022. "Variable frequency wind speed trend prediction system based on combined neural network and improved multi-objective optimization algorithm," Energy, Elsevier, vol. 254(PA).
  4. 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.
  5. Fang, Ping & Fu, Wenlong & Wang, Kai & Xiong, Dongzhen & Zhang, Kai, 2022. "A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 307(C).
  6. Sizhou Sun & Jingqi Fu & Ang Li, 2019. "A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches," Energies, MDPI, vol. 12(18), pages 1-22, September.
  7. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Man, Yi, 2022. "Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction," Energy, Elsevier, vol. 244(PB).
  8. Zhang, Yagang & Zhao, Yunpeng & Shen, Xiaoyu & Zhang, Jinghui, 2022. "A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 305(C).
  9. Yuqi Dong & Jianzhou Wang & Xinsong Niu & Bo Zeng, 2023. "Combined water quality forecasting system based on multiobjective optimization and improved data decomposition integration strategy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 260-287, March.
  10. Liang, Tao & Zhao, Qing & Lv, Qingzhao & Sun, Hexu, 2021. "A novel wind speed prediction strategy based on Bi-LSTM, MOOFADA and transfer learning for centralized control centers," Energy, Elsevier, vol. 230(C).
  11. Liu, Xingdou & Zhang, Li & Wang, Jiangong & Zhou, Yue & Gan, Wei, 2023. "A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data," Renewable Energy, Elsevier, vol. 211(C), pages 948-963.
  12. Zhang, Ziyuan & Wang, Jianzhou & Wei, Danxiang & Luo, Tianrui & Xia, Yurui, 2023. "A novel ensemble system for short-term wind speed forecasting based on Two-stage Attention-Based Recurrent Neural Network," Renewable Energy, Elsevier, vol. 204(C), pages 11-23.
  13. Liu, Hui & Duan, Zhu & Chen, Chao, 2020. "Wind speed big data forecasting using time-variant multi-resolution ensemble model with clustering auto-encoder," Applied Energy, Elsevier, vol. 280(C).
  14. 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).
  15. Wang, Jianzhou & Wang, Shiqi & Yang, Wendong, 2019. "A novel non-linear combination system for short-term wind speed forecast," Renewable Energy, Elsevier, vol. 143(C), pages 1172-1192.
  16. Jiang, Ping & Liu, Zhenkun & Niu, Xinsong & Zhang, Lifang, 2021. "A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting," Energy, Elsevier, vol. 217(C).
  17. Peng Zhang & Xin Ma & Kun She, 2019. "Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model," Sustainability, MDPI, vol. 11(21), pages 1-23, October.
  18. Gao, Yuyang & Wang, Jianzhou & Yang, Hufang, 2022. "A multi-component hybrid system based on predictability recognition and modified multi-objective optimization for ultra-short-term onshore wind speed forecasting," Renewable Energy, Elsevier, vol. 188(C), pages 384-401.
  19. Lv, Sheng-Xiang & Wang, Lin, 2022. "Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization," Applied Energy, Elsevier, vol. 311(C).
  20. Zhihao Shang & Quan Wen & Yanhua Chen & Bing Zhou & Mingliang Xu, 2022. "Wind Speed Forecasting Using Attention-Based Causal Convolutional Network and Wind Energy Conversion," Energies, MDPI, vol. 15(8), pages 1-23, April.
  21. Zhang, Wenyu & Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong, 2020. "Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting," Applied Energy, Elsevier, vol. 277(C).
  22. Nie, Ying & Liang, Ni & Wang, Jianzhou, 2021. "Ultra-short-term wind-speed bi-forecasting system via artificial intelligence and a double-forecasting scheme," Applied Energy, Elsevier, vol. 301(C).
  23. Yechi Zhang & Jianzhou Wang & Haiyan Lu, 2019. "Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting," Energies, MDPI, vol. 12(10), pages 1-27, May.
  24. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
  25. Chih-Yao Chang & Kuo-Ping Lin, 2020. "Developing Support Vector Machine with New Fuzzy Selection for the Infringement of a Patent Rights Problem," Mathematics, MDPI, vol. 8(8), pages 1-16, August.
  26. Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong & Liu, Zhenkun, 2021. "Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection," Applied Energy, Elsevier, vol. 301(C).
  27. 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.
  28. Yang, Rui & Liu, Hui & Nikitas, Nikolaos & Duan, Zhu & Li, Yanfei & Li, Ye, 2022. "Short-term wind speed forecasting using deep reinforcement learning with improved multiple error correction approach," Energy, Elsevier, vol. 239(PB).
  29. Wang, Jianzhou & Wang, Shuai & Zeng, Bo & Lu, Haiyan, 2022. "A novel ensemble probabilistic forecasting system for uncertainty in wind speed," Applied Energy, Elsevier, vol. 313(C).
  30. Zifa Liu & Xinyi Li & Haiyan Zhao, 2023. "Short-Term Wind Power Forecasting Based on Feature Analysis and Error Correction," Energies, MDPI, vol. 16(10), pages 1-24, May.
  31. Li, Ranran & Hu, Yucai & Heng, Jiani & Chen, Xueli, 2021. "A novel multiscale forecasting model for crude oil price time series," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
  32. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  33. Ai, Chunyu & He, Shan & Fan, Xiaochao & Wang, Weiqing, 2023. "Chaotic time series wind power prediction method based on OVMD-PE and improved multi-objective state transition algorithm," Energy, Elsevier, vol. 278(C).
  34. Muhammad, Yasir & Khan, Nusrat & Awan, Saeed Ehsan & Raja, Muhammad Asif Zahoor & Chaudhary, Naveed Ishtiaq & Kiani, Adiqa Kausar & Ullah, Farman & Shu, Chi-Min, 2022. "Fractional memetic computing paradigm for reactive power management involving wind-load chaos and uncertainties," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
  35. Jiang, Ping & Li, Ranran & Liu, Ningning & Gao, Yuyang, 2020. "A novel composite electricity demand forecasting framework by data processing and optimized support vector machine," Applied Energy, Elsevier, vol. 260(C).
  36. Wang, Shuai & Wang, Jianzhou & Lu, Haiyan & Zhao, Weigang, 2021. "A novel combined model for wind speed prediction – Combination of linear model, shallow neural networks, and deep learning approaches," Energy, Elsevier, vol. 234(C).
  37. Yang, Wendong & Sun, Shaolong & Hao, Yan & Wang, Shouyang, 2022. "A novel machine learning-based electricity price forecasting model based on optimal model selection strategy," Energy, Elsevier, vol. 238(PC).
  38. Yingya Zhou & Linwei Ma & Weidou Ni & Colin Yu, 2023. "Data Enrichment as a Method of Data Preprocessing to Enhance Short-Term Wind Power Forecasting," Energies, MDPI, vol. 16(5), pages 1-18, February.
  39. Shu, Zhiyong & Liang, Wenqing & Liu, Fan & Lei, Gang & Zheng, Xiaohong & Qian, Hua, 2022. "Diffusion characteristics of liquid hydrogen spills in a crossflow field: Prediction model and experiment," Applied Energy, Elsevier, vol. 323(C).
  40. Dong, Yingchao & Zhang, Hongli & Wang, Cong & Zhou, Xiaojun, 2021. "A novel hybrid model based on Bernstein polynomial with mixture of Gaussians for wind power forecasting," Applied Energy, Elsevier, vol. 286(C).
  41. Xu Ran & Chang Xu & Lei Ma & Feifei Xue, 2022. "Wind Power Interval Prediction with Adaptive Rolling Error Correction Based on PSR-BLS-QR," Energies, MDPI, vol. 15(11), pages 1-22, June.
  42. Wang, Jianzhou & An, Yining & Li, Zhiwu & Lu, Haiyan, 2022. "A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting," Energy, Elsevier, vol. 251(C).
  43. Wang, Kang & Wang, Jianzhou & Zeng, Bo & Lu, Haiyan, 2022. "An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization," Applied Energy, Elsevier, vol. 314(C).
  44. Wang, Jianzhou & Gao, Jialu & Wei, Danxiang, 2022. "Electric load prediction based on a novel combined interval forecasting system," Applied Energy, Elsevier, vol. 322(C).
  45. Pan, Xiaoxin & Wang, Long & Wang, Zhongju & Huang, Chao, 2022. "Short-term wind speed forecasting based on spatial-temporal graph transformer networks," Energy, Elsevier, vol. 253(C).
  46. Liu, Zhenkun & Jiang, Ping & Zhang, Lifang & Niu, Xinsong, 2020. "A combined forecasting model for time series: Application to short-term wind speed forecasting," Applied Energy, Elsevier, vol. 259(C).
  47. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
  48. Wang, Jianzhou & Niu, Xinsong & Zhang, Linyue & Lv, Mengzheng, 2021. "Point and interval prediction for non-ferrous metals based on a hybrid prediction framework," Resources Policy, Elsevier, vol. 73(C).
  49. Wang, Hao & Ye, Jingzhen & Huang, Linxuan & Wang, Qiang & Zhang, Haohua, 2023. "A multivariable hybrid prediction model of offshore wind power based on multi-stage optimization and reconstruction prediction," Energy, Elsevier, vol. 262(PA).
  50. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).
  51. Xinhao Liang & Feihu Hu & Xin Li & Lin Zhang & Hui Cao & Haiming Li, 2023. "Spatio-Temporal Wind Speed Prediction Based on Improved Residual Shrinkage Network," Sustainability, MDPI, vol. 15(7), pages 1-19, March.
  52. Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).
  53. Lu, Peng & Ye, Lin & Tang, Yong & Zhao, Yongning & Zhong, Wuzhi & Qu, Ying & Zhai, Bingxu, 2021. "Ultra-short-term combined prediction approach based on kernel function switch mechanism," Renewable Energy, Elsevier, vol. 164(C), pages 842-866.
  54. Wang, Jianzhou & Niu, Xinsong & Zhang, Lifang & Liu, Zhenkun & Wei, Danxiang, 2022. "The influence of international oil prices on the exchange rates of oil exporting countries: Based on the hybrid copula function," Resources Policy, Elsevier, vol. 77(C).
  55. Shang, Zhihao & He, Zhaoshuang & Chen, Yao & Chen, Yanhua & Xu, MingLiang, 2022. "Short-term wind speed forecasting system based on multivariate time series and multi-objective optimization," Energy, Elsevier, vol. 238(PC).
  56. Liu, Ling & Wang, Jujie, 2021. "Super multi-step wind speed forecasting system with training set extension and horizontal–vertical integration neural network," Applied Energy, Elsevier, vol. 292(C).
  57. Yin, Hao & Ou, Zuhong & Huang, Shengquan & Meng, Anbo, 2019. "A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition," Energy, Elsevier, vol. 189(C).
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