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Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system. (2024). Zhao, Zheng ; Zhang, Yagang ; Cheng, Xiaodan ; Wang, Tong.
In: Energy.
RePEc:eee:energy:v:292:y:2024:i:c:s0360544224002639.

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  1. A Wind Speed Prediction Method Based on Signal Decomposition Technology Deep Learning Model. (2025). Du, Jie ; Liu, Yubao ; Pan, Linlin ; Chen, Shuaizhi.
    In: Energies.
    RePEc:gam:jeners:v:18:y:2025:i:5:p:1136-:d:1599642.

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  2. Advancing renewable energy scenarios with graph theory and ensemble meta-optimized approach. (2025). Bafti, Amin Arjmand ; Rezaei, Mohsen.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:218:y:2025:i:c:s1364032125004794.

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  3. Probability density function based adaptive ensemble learning with global convergence for wind power prediction. (2024). Zhou, Chengyu ; Jia, LI ; Li, Jianfang.
    In: Energy.
    RePEc:eee:energy:v:312:y:2024:i:c:s0360544224033516.

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  4. A reconstruction-based secondary decomposition-ensemble framework for wind power forecasting. (2024). Zhang, Guowei ; Liu, DA ; Yang, DI ; Cheng, Runkun.
    In: Energy.
    RePEc:eee:energy:v:308:y:2024:i:c:s0360544224026690.

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  17. A new chaotic time series hybrid prediction method of wind power based on EEMD-SE and full-parameters continued fraction. (2017). Fan, Wenhui ; Ma, Ping ; Zhang, Hongli ; Wang, Cong.
    In: Energy.
    RePEc:eee:energy:v:138:y:2017:i:c:p:977-990.

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  18. Short-term wind speed forecasting using a hybrid model. (2017). Wang, Yun ; Jiang, Ping.
    In: Energy.
    RePEc:eee:energy:v:119:y:2017:i:c:p:561-577.

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  19. Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting. (2016). Ma, Xuejiao ; Liu, Dandan.
    In: Energies.
    RePEc:gam:jeners:v:9:y:2016:i:8:p:640-:d:76019.

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  20. Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting. (2016). Filik, Tansu.
    In: Energies.
    RePEc:gam:jeners:v:9:y:2016:i:3:p:168-:d:65196.

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  21. Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model. (2016). Rivera, Wilfrido ; Campos-Amezcua, Rafael ; Cadenas, Erasmo ; Heard, Christopher.
    In: Energies.
    RePEc:gam:jeners:v:9:y:2016:i:2:p:109-:d:63927.

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  22. Transfer learning for short-term wind speed prediction with deep neural networks. (2016). Zhou, Yucan ; Zhang, Rujia ; Hu, Qinghua.
    In: Renewable Energy.
    RePEc:eee:renene:v:85:y:2016:i:c:p:83-95.

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  23. A novel bidirectional mechanism based on time series model for wind power forecasting. (2016). Li, Zhi ; Zhao, Yongning ; Lang, Yansheng ; Song, Xuri ; Ye, Lin ; Su, Jian.
    In: Applied Energy.
    RePEc:eee:appene:v:177:y:2016:i:c:p:793-803.

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  24. Hybrid Wind Speed Prediction Based on a Self-Adaptive ARIMAX Model with an Exogenous WRF Simulation. (2015). Su, Zhongyue ; An, Ning ; Liu, Liwei ; Zhao, Jing.
    In: Energies.
    RePEc:gam:jeners:v:9:y:2015:i:1:p:7-:d:61216.

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  25. Wind speed prediction in the mountainous region of India using an artificial neural network model. (2015). Chandel, S. S. ; Ramasamy, P. ; Yadav, Amit Kumar.
    In: Renewable Energy.
    RePEc:eee:renene:v:80:y:2015:i:c:p:338-347.

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  26. A self-adaptive hybrid approach for wind speed forecasting. (2015). Zhang, Yixin ; Ma, Kailiang ; Wang, Jianzhou ; Hu, Jianming.
    In: Renewable Energy.
    RePEc:eee:renene:v:78:y:2015:i:c:p:374-385.

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  27. Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China. (2015). Qin, Shanshan ; Zhou, Qingping ; Wang, Jianzhou ; Jiang, Haiyan.
    In: Renewable Energy.
    RePEc:eee:renene:v:76:y:2015:i:c:p:91-101.

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  28. Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA. (2015). Shukur, Osamah Basheer ; Lee, Muhammad Hisyam.
    In: Renewable Energy.
    RePEc:eee:renene:v:76:y:2015:i:c:p:637-647.

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  29. Reliability measures for indexed semi-Markov chains applied to wind energy production. (2015). Damico, Guglielmo ; Prattico, Flavio ; Petroni, Filippo.
    In: Reliability Engineering and System Safety.
    RePEc:eee:reensy:v:144:y:2015:i:c:p:170-177.

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  30. A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting. (2015). Arunraj, Nari Sivanandam ; Ahrens, Diane.
    In: International Journal of Production Economics.
    RePEc:eee:proeco:v:170:y:2015:i:pa:p:321-335.

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  31. Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea. (2015). Koo, Junmo ; Shim, Joon Hyung ; Choi, Hyung Jong ; Han, Gwon Deok .
    In: Energy.
    RePEc:eee:energy:v:93:y:2015:i:p2:p:1296-1302.

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  32. A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China. (2015). Wang, Yun ; Wei, Xiang.
    In: Energy.
    RePEc:eee:energy:v:91:y:2015:i:c:p:556-572.

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  33. A study of single multiplicative neuron model with nonlinear filters for hourly wind speed prediction. (2015). Du, Zhaoping ; Chang, Yanchao ; Su, Xunliang ; Wu, Xuedong ; Fan, Shaosheng ; Zeng, Qingjun ; Zhu, Zhiyu.
    In: Energy.
    RePEc:eee:energy:v:88:y:2015:i:c:p:194-201.

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  34. Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks. (2015). Li, Yan-Fei ; Liang, Xi-Feng ; Liu, Hui ; Tian, Hong-Qi .
    In: Applied Energy.
    RePEc:eee:appene:v:157:y:2015:i:c:p:183-194.

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  35. The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China. (2015). Wang, Yun ; Jiang, Ping.
    In: Applied Energy.
    RePEc:eee:appene:v:143:y:2015:i:c:p:472-488.

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  36. Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting. (2014). Jimenez-Come, M. J. ; Ruiz-Aguilar, J. J. ; Turias, I. J..
    In: Transportation Research Part E: Logistics and Transportation Review.
    RePEc:eee:transe:v:67:y:2014:i:c:p:1-13.

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  37. A review of combined approaches for prediction of short-term wind speed and power. (2014). Uzunoglu, M. ; Tascikaraoglu, A..
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:34:y:2014:i:c:p:243-254.

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  38. Wind energy resource in Northern Mexico. (2014). Saldaa-Flores, R. ; Hernandez-Escobedo, Q. ; Manzano-Agugliaro, F. ; Rodriguez-Garcia, E. R..
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:32:y:2014:i:c:p:890-914.

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  39. Current status and future advances for wind speed and power forecasting. (2014). Broadwater, Robert P. ; Jung, Jaesung.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:31:y:2014:i:c:p:762-777.

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  40. Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. (2014). Fan, Leilei ; Wang, Hui ; Niu, Dongxiao ; Liu, DA.
    In: Renewable Energy.
    RePEc:eee:renene:v:62:y:2014:i:c:p:592-597.

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  41. A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China. (2014). Xiong, Shenghua ; Wang, Jianzhou.
    In: Energy.
    RePEc:eee:energy:v:76:y:2014:i:c:p:526-541.

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  42. A time series-based approach for renewable energy modeling. (2013). Karanfil, Fatih ; Hocaoglu, Fatih Onur.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:28:y:2013:i:c:p:204-214.

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  43. Very short-term wind speed forecasting with Bayesian structural break model. (2013). Song, Zhe ; Jiang, YU ; Kusiak, Andrew.
    In: Renewable Energy.
    RePEc:eee:renene:v:50:y:2013:i:c:p:637-647.

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  44. A new strategy for predicting short-term wind speed using soft computing models. (2012). Senjyu, Tomonobu ; Mandal, Paras ; Kaye, Mary E. ; Chang, Liuchen ; Meng, Julian ; Haque, Ashraf U..
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:16:y:2012:i:7:p:4563-4573.

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  45. Evaluation of hybrid forecasting approaches for wind speed and power generation time series. (2012). Zheng, Songtao ; Guo, Jinmei ; Shi, Jing.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:16:y:2012:i:5:p:3471-3480.

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  46. A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks. (2012). Li, Yan-Fei ; Liu, Hui ; Tian, Hong-Qi ; Chen, Chao.
    In: Renewable Energy.
    RePEc:eee:renene:v:48:y:2012:i:c:p:545-556.

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  47. Performance analysis of four modified approaches for wind speed forecasting. (2012). Zhao, Weigang ; Zhang, Wenyu ; Shen, Lin ; Wu, Jie ; Wang, Jianzhou.
    In: Applied Energy.
    RePEc:eee:appene:v:99:y:2012:i:c:p:324-333.

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  48. Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction. (2012). Li, Yan-Fei ; Liu, Hui ; Tian, Hong-Qi .
    In: Applied Energy.
    RePEc:eee:appene:v:98:y:2012:i:c:p:415-424.

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  49. Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods. (2011). de Giorgi, Maria Grazia ; Tarantino, Marco ; Ficarella, Antonio.
    In: Energy.
    RePEc:eee:energy:v:36:y:2011:i:7:p:3968-3978.

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  50. Evaluating alternative offering strategies for wind producers in a pool. (2011). Rahimiyan, Morteza ; Morales, Juan M. ; Conejo, Antonio J..
    In: Applied Energy.
    RePEc:eee:appene:v:88:y:2011:i:12:p:4918-4926.

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