create a website

A novel hybrid forecasting model with feature selection and deep learning for wind speed research. (2024). Wang, Jianzhou ; Zhang, Haitao ; Chen, Xuejun.
In: Journal of Forecasting.
RePEc:wly:jforec:v:43:y:2024:i:5:p:1682-1705.

Full description at Econpapers || Download paper

Cited: 0

Citations received by this document

Cites: 70

References cited by this document

Cocites: 27

Documents which have cited the same bibliography

Coauthors: 0

Authors who have wrote about the same topic

Citations

Citations received by this document

    This document has not been cited yet.

References

References cited by this document

  1. Altan, A., Karasu, S., & Zio, E. (2021). A new hybrid model for wind speed forecasting combining long short‐term memory neural network, decomposition methods and grey wolf optimizer. Applied Soft Computing, 100, 106996. https://doi.org/10.1016/J.ASOC.2020.106996.
    Paper not yet in RePEc: Add citation now
  2. Bashir, H., Sibtain, M., Hanay, Ö., Azam, M. I., Qurat‐ul‐Ain, & Saleem, S. (2023). Decomposition and Harris hawks optimized multivariate wind speed forecasting utilizing sequence2sequence‐based spatiotemporal attention. Energy, 278, 127933. https://doi.org/10.1016/J.ENERGY.2023.127933.

  3. Broomhead, D. S., & King, G. P. (1986). Extracting qualitative dynamics from experimental data. Physica D: Nonlinear Phenomena, 20(2–3), 217–236. https://doi.org/10.1016/0167-2789(86)90031-X.
    Paper not yet in RePEc: Add citation now
  4. Cai, H., Jia, X., Feng, J., Yang, Q., Li, W., Li, F., & Lee, J. (2021). A unified Bayesian filtering framework for multi‐horizon wind speed prediction with improved accuracy. Renewable Energy, 178, 709–719. https://doi.org/10.1016/J.RENENE.2021.06.092.

  5. Casella, L. (2019). Wind speed reconstruction using a novel multivariate probabilistic method and multiple linear regression: Advantages compared to the single correlation approach. Journal of Wind Engineering and Industrial Aerodynamics, 191, 252–265. https://doi.org/10.1016/J.JWEIA.2019.05.020.
    Paper not yet in RePEc: Add citation now
  6. Chen, X. J., Zhao, J., Jia, X. Z., & Li, Z. L. (2021). Multi‐step wind speed forecast based on sample clustering and an optimized hybrid system. Renewable Energy, 165, 595–611. https://doi.org/10.1016/j.renene.2020.11.038.
    Paper not yet in RePEc: Add citation now
  7. Chen, X., Jin, S., Qin, S., & Li, L. (2015). Short‐term wind speed forecasting study and its application using a hybrid model optimized by cuckoo search. Mathematical Problems in Engineering, 2015, 1–18. https://doi.org/10.1155/2015/608597.
    Paper not yet in RePEc: Add citation now
  8. Chen, X., Zhao, J., Hu, W., & Yang, Y. (2014). Short‐term wind speed forecasting using decomposition‐based neural networks combining abnormal detection method. Abstract and Applied Analysis, 2014, 1–21. https://doi.org/10.1155/2014/984268.
    Paper not yet in RePEc: Add citation now
  9. Deng, Y., & Gao, Q. (2020). A study on e‐commerce customer segmentation management based on improved K‐means algorithm. Information Systems and e‐Business Management, 18(4), 497–510. https://doi.org/10.1007/S10257-018-0381-3/TABLES/5.
    Paper not yet in RePEc: Add citation now
  10. Dong, Y., Li, J., Liu, Z., Niu, X., & Wang, J. (2022). Ensemble wind speed forecasting system based on optimal model adaptive selection strategy: Case study in China. Sustainable Energy Technologies and Assessments, 53(PB), 102535. https://doi.org/10.1016/j.seta.2022.102535.
    Paper not yet in RePEc: Add citation now
  11. Duarte Jacondino, W., Nascimento, A. L. S., Calvetti, L., Fisch, G., Augustus Assis Beneti, C., & da Paz, S. R. (2021). Hourly day‐ahead wind power forecasting at two wind farms in Northeast Brazil using WRF model. Energy, 230, 120841. https://doi.org/10.1016/j.energy.2021.120841.

  12. Fan, F., Bell, K., Hill, D., & Infield, D. (2015). Wind forecasting using kriging and vector auto‐regressive models for dynamic line rating studies. 2015 IEEE Eindhoven PowerTech, PowerTech 2015. https://doi.org/10.1109/PTC.2015.7232348.
    Paper not yet in RePEc: Add citation now
  13. Flandrin, P., Torres, E., & Colominas, M. A. (2011). A complete ensemble empirical mode decomposition with adaptive noise. 4144–4147.
    Paper not yet in RePEc: Add citation now
  14. Fu, W., Fang, P., Wang, K., Li, Z., Xiong, D., & Zhang, K. (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, 179, 1122–1139. https://doi.org/10.1016/J.RENENE.2021.07.119.
    Paper not yet in RePEc: Add citation now
  15. Fu, W., Fu, Y., Li, B., Zhang, H., Zhang, X., & Liu, J. (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, 348, 121587. https://doi.org/10.1016/J.APENERGY.2023.121587.

  16. He, F., Zhou, J., Feng, Z. K., Liu, G., & Yang, Y. (2019). A hybrid short‐term load forecasting model based on variational mode decomposition and long short‐term memory networks considering relevant factors with Bayesian optimization algorithm. Applied Energy, 237, 103–116. https://doi.org/10.1016/J.APENERGY.2019.01.055.
    Paper not yet in RePEc: Add citation now
  17. Herrera, F., Herrera‐Viedma, E., & Chiclana, F. (2001). Multiperson decision‐making based on multiplicative preference relations. European Journal of Operational Research, 129(2), 372–385. https://doi.org/10.1016/S0377-2217(99)00197-6.

  18. Hu, H., Wang, L., & Tao, R. (2021). Wind speed forecasting based on variational mode decomposition and improved echo state network. Renewable Energy, 164, 729–751. https://doi.org/10.1016/J.RENENE.2020.09.109.

  19. Hu, H., Wang, L., Zhang, D., & Ling, L. (2023). Rolling decomposition method in fusion with echo state network for wind speed forecasting. Renewable Energy, 216, 119101. https://doi.org/10.1016/J.RENENE.2023.119101.

  20. Hu, Q., Zhang, R., & Zhou, Y. (2016). Transfer learning for short‐term wind speed prediction with deep neural networks. Renewable Energy, 85, 83–95. https://doi.org/10.1016/J.RENENE.2015.06.034.
    Paper not yet in RePEc: Add citation now
  21. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Snin, H. H., Zheng, Q., Yen, N. C., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non‐stationary time series analysis. RSPSA, 454(1971), 903–998. https://doi.org/10.1098/RSPA.1998.0193.
    Paper not yet in RePEc: Add citation now
  22. Jahangir, H., Golkar, M. A., Alhameli, F., Mazouz, A., Ahmadian, A., & Elkamel, A. (2020). Short‐term wind speed forecasting framework based on stacked denoising auto‐encoders with rough ANN. Sustainable Energy Technologies and Assessments, 38(June 2019), 100601. https://doi.org/10.1016/j.seta.2019.100601.
    Paper not yet in RePEc: Add citation now
  23. Jiang, P., Wang, B., Li, H., & Lu, H. (2019). Modeling for chaotic time series based on linear and nonlinear framework : Application to wind speed forecasting. Energy, 173, 468–482. https://doi.org/10.1016/j.energy.2019.02.080.

  24. Kitto, G. B. (1968). Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. 407–408.
    Paper not yet in RePEc: Add citation now
  25. Kuo, H. C., & Chang, H. K. (2003). A real‐time shipboard fire‐detection system based on grey‐fuzzy algorithms. Fire Safety Journal, 38(4), 341–363. https://doi.org/10.1016/S0379-7112(02)00088-7.
    Paper not yet in RePEc: Add citation now
  26. Lahouar, A., & Ben Hadj Slama, J. (2017). Hour‐ahead wind power forecast based on random forests. Renewable Energy, 109, 529–541. https://doi.org/10.1016/J.RENENE.2017.03.064.
    Paper not yet in RePEc: Add citation now
  27. Li, X., Peng, L., Yao, X., Cui, S., Hu, Y., You, C., & Chi, T. (2017). Long short‐term memory neural network for air pollutant concentration predictions: Method development and evaluation. Environmental Pollution, 231, 997–1004. https://doi.org/10.1016/j.envpol.2017.08.114.
    Paper not yet in RePEc: Add citation now
  28. Liu, G., Wang, A., & Zhao, Y. (2011). An efficient image segmentation method based on fuzzy particle swarm optimization and Markov random field model. 7th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2011. https://doi.org/10.1109/WICOM.2011.6040554.
    Paper not yet in RePEc: Add citation now
  29. Liu, K., Cheng, J., & Yi, J. (2022). Copper price forecasted by hybrid neural network with Bayesian optimization and wavelet transform. Resources Policy, 75, 102520. https://doi.org/10.1016/J.RESOURPOL.2021.102520.
    Paper not yet in RePEc: Add citation now
  30. Liu, M., Cao, Z., Zhang, J., Wang, L., Huang, C., & Luo, X. (2020). Short‐term wind speed forecasting based on the Jaya‐SVM model. International Journal of Electrical Power & Energy Systems, 121, 106056. https://doi.org/10.1016/J.IJEPES.2020.106056.
    Paper not yet in RePEc: Add citation now
  31. Liu, X., Lin, Z., & Feng, Z. (2021). Short‐term offshore wind speed forecast by seasonal ARIMA—A comparison against GRU and LSTM. Energy, 227, 120492. https://doi.org/10.1016/J.ENERGY.2021.120492.
    Paper not yet in RePEc: Add citation now
  32. Lv, M., Li, J., Niu, X., & Wang, J. (2022). Novel deterministic and probabilistic combined system based on deep learning and self‐improved optimization algorithm for wind speed forecasting. Sustainable Energy Technologies and Assessments, 52(PB), 102186. https://doi.org/10.1016/j.seta.2022.102186.
    Paper not yet in RePEc: Add citation now
  33. Ma, D., & Duan, Q. (2022). A hybrid‐strategy‐improved butterfly optimization algorithm applied to the node coverage problem of wireless sensor networks. Mathematical Biosciences and Engineering, 19(4), 3928–3952. https://doi.org/10.3934/MBE.2022181.
    Paper not yet in RePEc: Add citation now
  34. Méndez‐Gordillo, A. R., Campos‐Amezcua, R., & Cadenas, E. (2022). Wind speed forecasting using a hybrid model considering the turbulence of the airflow. Renewable Energy, 196, 422–431. https://doi.org/10.1016/j.renene.2022.06.139.
    Paper not yet in RePEc: Add citation now
  35. Moghram, I., & Rahman, S. (1989). Analysis and evaluation of five short‐term load forecasting techniques. IEEE Power Engineering Review, 9(11), 42–43. https://doi.org/10.1109/MPER.1989.4310383.
    Paper not yet in RePEc: Add citation now
  36. Naik, J., Dash, P. K., & Dhar, S. (2019). A multi‐objective wind speed and wind power prediction interval forecasting using variational modes decomposition based multi‐kernel robust ridge regression. Renewable Energy, 136, 701–731. https://doi.org/10.1016/J.RENENE.2019.01.006.

  37. Noorollahi, Y., Jokar, M. A., & Kalhor, A. (2016). Using artificial neural networks for temporal and spatial wind speed forecasting in Iran. Energy Conversion and Management, 115, 17–25. https://doi.org/10.1016/J.ENCONMAN.2016.02.041.
    Paper not yet in RePEc: Add citation now
  38. Qin, J., Yang, J., Chen, Y., Ye, Q., & Li, H. (2021). Two‐stage short‐term wind power forecasting algorithm using different feature‐learning models. Fundamental Research, 1(4), 472–481. https://doi.org/10.1016/J.FMRE.2021.06.010.
    Paper not yet in RePEc: Add citation now
  39. Sett, S. K., Hazra, S., & Ghosh, A. (2020). A fuzzy clustering algorithm influenced by validity indices (FCVI) for recognizing the differentially expressed cancer mediating genes. Meta Gene, 23, 100615. https://doi.org/10.1016/J.MGENE.2019.100615.
    Paper not yet in RePEc: Add citation now
  40. Shang, Z., Chen, Y., Chen, Y., Guo, Z., & Yang, Y. (2023). Decomposition‐based wind speed forecasting model using causal convolutional network and attention mechanism. Expert Systems with Applications, 223, 119878. https://doi.org/10.1016/J.ESWA.2023.119878.
    Paper not yet in RePEc: Add citation now
  41. Shen, Z., Fan, X., Zhang, L., & Yu, H. (2022). Wind speed prediction of unmanned sailboat based on CNN and LSTM hybrid neural network. Ocean Engineering, 254, 111352. https://doi.org/10.1016/J.OCEANENG.2022.111352.
    Paper not yet in RePEc: Add citation now
  42. Singh, S. N., & Mohapatra, A. (2019). Repeated wavelet transform based ARIMA model for very short‐term wind speed forecasting. Renewable Energy, 136, 758–768. https://doi.org/10.1016/j.renene.2019.01.031.
    Paper not yet in RePEc: Add citation now
  43. Soman, S. S., Zareipour, H., Member, S., Malik, O., & Fellow, L. (2010). A review of wind power and wind speed forecasting methods with different time horizons. 1–8. papers3:publication/uuid/01A9077B‐50C8‐47AB‐9F17‐B4E94F80DFDF.
    Paper not yet in RePEc: Add citation now
  44. Sun, W., & Liu, M. (2016). Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China. Energy Conversion and Management, 114, 197–208. https://doi.org/10.1016/J.ENCONMAN.2016.02.022.
    Paper not yet in RePEc: Add citation now
  45. Syama, S., Ramprabhakar, J., Anand, R., & Guerrero, J. M. (2023). A hybrid extreme learning machine model with Lévy flight chaotic whale optimization algorithm for wind speed forecasting. Results in Engineering, 19, 101274. https://doi.org/10.1016/J.RINENG.2023.101274.
    Paper not yet in RePEc: Add citation now
  46. Wang, H. Z., Li, G. Q., Wang, G. B., Peng, J. C., Jiang, H., & Liu, Y. T. (2017). Deep learning based ensemble approach for probabilistic wind power forecasting. Applied Energy, 188, 56–70. https://doi.org/10.1016/J.APENERGY.2016.11.111.

  47. Wang, H., Han, S., Liu, Y., Yan, J., & Li, L. (2019). Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system. Applied Energy, 237, 1–10. https://doi.org/10.1016/J.APENERGY.2018.12.076.

  48. Wang, J., Wang, Y., Li, Z., Li, H., & Yang, H. (2020). A combined framework based on data preprocessing, neural networks and multi‐tracker optimizer for wind speed prediction. Sustainable Energy Technologies and Assessments, 40(March), 100757. https://doi.org/10.1016/j.seta.2020.100757.
    Paper not yet in RePEc: Add citation now
  49. Wang, J., Zhang, L., Niu, X., & Liu, Z. (2020). Effects of PM2.5 on health and economic loss: Evidence from Beijing‐Tianjin‐Hebei region of China. Journal of Cleaner Production, 257, 120605. https://doi.org/10.1016/j.jclepro.2020.120605.
    Paper not yet in RePEc: Add citation now
  50. Wang, Y., Chen, T., Zhou, S., Zhang, F., Zou, R., & Hu, Q. (2023). An improved wavenet network for multi‐step‐ahead wind energy forecasting. Energy Conversion and Management, 278, 116709. https://doi.org/10.1016/J.ENCONMAN.2023.116709.
    Paper not yet in RePEc: Add citation now
  51. Wang, Y., Wang, J., Li, Z., Yang, H., & Li, H. (2021). Design of a combined system based on two‐stage data preprocessing and multi‐objective optimization for wind speed prediction. Energy, 231, 121125. https://doi.org/10.1016/j.energy.2021.121125.
    Paper not yet in RePEc: Add citation now
  52. Wu, J., Chen, X. Y., Zhang, H., Xiong, L. D., Lei, H., & Deng, S. H. (2019). Hyperparameter optimization for machine learning models based on Bayesian optimization. Journal of Electronic Science and Technology, 17(1), 26–40. https://doi.org/10.11989/JEST.1674-862X.80904120.
    Paper not yet in RePEc: Add citation now
  53. Yang, D., Sharma, V., Ye, Z., Lim, L. I., Zhao, L., & Aryaputera, A. W. (2015). Forecasting of global horizontal irradiance by exponential smoothing, using decompositions. Energy, 81, 111–119. https://doi.org/10.1016/J.ENERGY.2014.11.082.

  54. Yang, W., Hao, M., & Hao, Y. (2023). Innovative ensemble system based on mixed frequency modeling for wind speed point and interval forecasting. Information Sciences, 622, 560–586. https://doi.org/10.1016/J.INS.2022.11.145.
    Paper not yet in RePEc: Add citation now
  55. Yang, W., Tian, Z., & Hao, Y. (2022). A novel ensemble model based on artificial intelligence and mixed‐frequency techniques for wind speed forecasting. Energy Conversion and Management, 252(November 2021), 115086. https://doi.org/10.1016/j.enconman.2021.115086.
    Paper not yet in RePEc: Add citation now
  56. Yang, W., Wang, J., Niu, T., & Du, P. (2019). A hybrid forecasting system based on a dual decomposition strategy and multi‐objective optimization for electricity price forecasting. Applied Energy, 235(February 2018), 1205–1225. https://doi.org/10.1016/j.apenergy.2018.11.034.

  57. Yang, W., Wang, J., Zhang, K., & Hao, Y. (2023). A novel air pollution forecasting, health effects, and economic cost assessment system for environmental management: From a new perspective of the district‐level. Journal of Cleaner Production, 417, 138027. https://doi.org/10.1016/J.JCLEPRO.2023.138027.
    Paper not yet in RePEc: Add citation now
  58. Ye, L., Zhao, Y., Zeng, C., & Zhang, C. (2017). Short‐term wind power prediction based on spatial model. Renewable Energy, 101, 1067–1074. https://doi.org/10.1016/j.renene.2016.09.069.
    Paper not yet in RePEc: Add citation now
  59. Yeh, J. R., Shieh, J. S., & Huang, N. E. (2010). Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method. Advances in Adaptive Data Analysis, 2(2), 135–156. https://doi.org/10.1142/S1793536910000422.
    Paper not yet in RePEc: Add citation now
  60. Yu, C., Li, Y., Bao, Y., Tang, H., & Zhai, G. (2018). A novel framework for wind speed prediction based on recurrent neural networks and support vector machine. Energy Conversion and Management, 178, 137–145. https://doi.org/10.1016/J.ENCONMAN.2018.10.008.
    Paper not yet in RePEc: Add citation now
  61. Zhang, J., Yan, J., Infield, D., Liu, Y., & Lien, F.‐s. (2019). Short‐term forecasting and uncertainty analysis of wind turbine power based on long short‐term memory network and Gaussian mixture model. Applied Energy, 241(January), 229–244. https://doi.org/10.1016/j.apenergy.2019.03.044.
    Paper not yet in RePEc: Add citation now
  62. Zhang, S., Wang, C., Liao, P., Xiao, L., & Fu, T. (2022). Wind speed forecasting based on model selection, fuzzy cluster, and multi‐objective algorithm and wind energy simulation by Betz's theory. Expert Systems with Applications, 193, 116509. https://doi.org/10.1016/J.ESWA.2022.116509.
    Paper not yet in RePEc: Add citation now
  63. Zhang, Y., Zhao, Y., Kong, C., & Chen, B. (2020). A new prediction method based on VMD‐PRBF‐ARMA‐E model considering wind speed characteristic. Energy Conversion and Management, 203(June 2019), 112254. https://doi.org/10.1016/j.enconman.2019.112254.
    Paper not yet in RePEc: Add citation now
  64. Zhao, J., Guo, Z. H., Su, Z. Y., Zhao, Z. Y., Xiao, X., & Liu, F. (2016). An improved multi‐step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed. Applied Energy, 162, 808–826. https://doi.org/10.1016/j.apenergy.2015.10.145.

  65. Zhao, L. T., Miao, J., Qu, S., & Chen, X. H. (2021). A multi‐factor integrated model for carbon price forecasting: Market interaction promoting carbon emission reduction. Science of the Total Environment, 796, 149110. https://doi.org/10.1016/j.scitotenv.2021.149110.
    Paper not yet in RePEc: Add citation now
  66. Zhao, X., Wang, C., Su, J., & Wang, J. (2019). Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system. Renewable Energy, 134, 681–697. https://doi.org/10.1016/j.renene.2018.11.061.

  67. Zheng, Y., Dong, B., Liu, Y., Tong, X., & Wang, L. (2021). Multistep wind speed forecasting based on a hybrid model of VMD and nonlinear autoregressive neural network. Journal of Mathematics, 2021, 1–9. https://doi.org/10.1155/2021/6644668.
    Paper not yet in RePEc: Add citation now
  68. Zhou, Q., Wang, C., & Zhang, G. (2019). Hybrid forecasting system based on an optimal model selection strategy for different wind speed forecasting problems. Applied Energy, 250, 1559–1580. https://doi.org/10.1016/J.APENERGY.2019.05.016.

  69. Zhu, S., Qiu, X., Yin, Y., Fang, M., Liu, X., Zhao, X., & Shi, Y. (2019). Two‐step‐hybrid model based on data preprocessing and intelligent optimization algorithms (CS and GWO) for NO2 and SO2 forecasting. Atmospheric Pollution Research, 10(4), 1326–1335. https://doi.org/10.1016/j.apr.2019.03.004.
    Paper not yet in RePEc: Add citation now
  70. Zhu, S., Yuan, X., Xu, Z., Luo, X., & Zhang, H. (2019). Gaussian mixture model coupled recurrent neural networks for wind speed interval forecast. Energy Conversion and Management, 198, 111772. https://doi.org/10.1016/J.ENCONMAN.2019.06.083.
    Paper not yet in RePEc: Add citation now

Cocites

Documents in RePEc which have cited the same bibliography

  1. Improving active power regulation for wind turbine by phase leading cascaded error-based active disturbance rejection control and multi-objective optimization. (2025). Li, Xuehan ; Liu, Jizhen ; Fang, Fang ; Wang, Wei ; Chen, Zhe.
    In: Renewable Energy.
    RePEc:eee:renene:v:243:y:2025:i:c:s0960148125002915.

    Full description at Econpapers || Download paper

  2. A multi-scale component feature learning framework based on CNN-BiGRU and online sequential regularized extreme learning machine for wind speed prediction. (2025). Wang, Zheng ; Zheng, Yongshun ; Zhang, Chu ; Peng, Tian ; Zhao, Huanyu ; Yao, Junhao.
    In: Renewable Energy.
    RePEc:eee:renene:v:242:y:2025:i:c:s0960148125000898.

    Full description at Econpapers || Download paper

  3. Probabilistic power forecasting for wind farm clusters using Moran-Graph network with posterior feedback attention mechanism. (2025). Qu, Zhijian ; Hou, Xinxing ; Huang, Shixun ; Li, DI ; He, Yang ; Meng, Yan.
    In: Energy.
    RePEc:eee:energy:v:328:y:2025:i:c:s0360544225022005.

    Full description at Econpapers || Download paper

  4. A modular multi-step forecasting method for offshore wind power clusters. (2025). Yu, Sheng ; He, Bin ; Fang, Lei.
    In: Applied Energy.
    RePEc:eee:appene:v:380:y:2025:i:c:s0306261924024449.

    Full description at Econpapers || Download paper

  5. A novel hybrid forecasting model with feature selection and deep learning for wind speed research. (2024). Wang, Jianzhou ; Zhang, Haitao ; Chen, Xuejun.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:43:y:2024:i:5:p:1682-1705.

    Full description at Econpapers || Download paper

  6. Heavy rainfall event in Nova Friburgo (Brazil): numerical sensitivity analysis using different parameterization combinations in the WRF model. (2024). Silva, Fabricio Polifke ; Alvarez, Maria Gertrudes ; Veiga, Carolina.
    In: Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards.
    RePEc:spr:nathaz:v:120:y:2024:i:13:d:10.1007_s11069-024-06638-6.

    Full description at Econpapers || Download paper

  7. Brazilian wind energy generation potential using mixtures of Weibull distributions. (2024). , Tiago ; Alves, Silvio Fernando ; Felix, Kerolly Kedma ; da Silva, Jader ; Dos, Fabio Sandro.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:189:y:2024:i:pb:s1364032123008481.

    Full description at Econpapers || Download paper

  8. Multivariate short-term wind speed prediction based on PSO-VMD-SE-ICEEMDAN two-stage decomposition and Att-S2S. (2024). Sun, Xiaoying ; Liu, Haizhong.
    In: Energy.
    RePEc:eee:energy:v:305:y:2024:i:c:s0360544224020024.

    Full description at Econpapers || Download paper

  9. A novel model for ultra-short term wind power prediction based on Vision Transformer. (2024). Fu, Xiaomengting ; Yao, Qingtao ; Xiang, Ling ; Zhu, Guopeng.
    In: Energy.
    RePEc:eee:energy:v:294:y:2024:i:c:s0360544224006261.

    Full description at Econpapers || Download paper

  10. Interpretable wind speed forecasting with meteorological feature exploring and two-stage decomposition. (2024). Yu, Sihao ; Peng, LU ; Wang, Lin ; Wu, Binrong.
    In: Energy.
    RePEc:eee:energy:v:294:y:2024:i:c:s0360544224005541.

    Full description at Econpapers || Download paper

  11. An innovative interpretable combined learning model for wind speed forecasting. (2024). Li, Yanzhao ; Du, Pei ; Wang, Jianzhou ; Yang, Dongchuan.
    In: Applied Energy.
    RePEc:eee:appene:v:358:y:2024:i:c:s0306261923019177.

    Full description at Econpapers || Download paper

  12. Temporal collaborative attention for wind power forecasting. (2024). Wang, Zhijin ; Hu, Yue ; Zhao, Yuan ; Liu, Xiufeng ; Wu, Senzhen.
    In: Applied Energy.
    RePEc:eee:appene:v:357:y:2024:i:c:s0306261923018664.

    Full description at Econpapers || Download paper

  13. A Novel Ensemble Model Based on an Advanced Optimization Algorithm for Wind Speed Forecasting. (2023). Wang, Yukun ; Wei, Xiaoxue ; Li, Ranran ; Zhao, Aiying.
    In: Energies.
    RePEc:gam:jeners:v:16:y:2023:i:14:p:5281-:d:1190784.

    Full description at Econpapers || Download paper

  14. A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data. (2023). Zhou, Yue ; Liu, Xingdou ; Gan, Wei ; Zhang, LI ; Wang, Jiangong.
    In: Renewable Energy.
    RePEc:eee:renene:v:211:y:2023:i:c:p:948-963.

    Full description at Econpapers || Download paper

  15. Wind field simulation using WRF model in complex terrain: A sensitivity study with orthogonal design. (2023). Han, Yan ; Zhou, Pinhan ; Mi, Lihua ; Shen, Lian ; Li, Kai ; Cai, C S.
    In: Energy.
    RePEc:eee:energy:v:285:y:2023:i:c:s0360544223028050.

    Full description at Econpapers || Download paper

  16. Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model. (2023). Dong, Yunxuan ; Wu, Thomas ; Goh, Hui Hwang ; Zhu, Hongyu ; Zhang, Dongdong ; Chen, Baian.
    In: Energy.
    RePEc:eee:energy:v:285:y:2023:i:c:s0360544223021564.

    Full description at Econpapers || Download paper

  17. Dual-meta pool method for wind farm power forecasting with small sample data. (2023). Wei, LU ; Liu, Ling ; Wang, Jujie.
    In: Energy.
    RePEc:eee:energy:v:267:y:2023:i:c:s0360544222033904.

    Full description at Econpapers || Download paper

  18. Intelligent estimation of wind farm performance with direct and indirect ‘point’ forecasting approaches integrating several NWP models. (2023). Mathew, Sathyajith ; Leal, Joao ; Yakoub, Ghali.
    In: Energy.
    RePEc:eee:energy:v:263:y:2023:i:pd:s0360544222027797.

    Full description at Econpapers || Download paper

  19. A multivariable hybrid prediction model of offshore wind power based on multi-stage optimization and reconstruction prediction. (2023). Wang, Qiang ; Huang, Linxuan ; Ye, Jingzhen ; Zhang, Haohua.
    In: Energy.
    RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222023106.

    Full description at Econpapers || Download paper

  20. Cooperative mechanisms for multi-energy complementarity in the electricity spot market. (2023). Han, Zhixin ; Yang, Peiwen ; Lei, Leyao ; Fang, Debin.
    In: Energy Economics.
    RePEc:eee:eneeco:v:127:y:2023:i:pb:s0140988323006060.

    Full description at Econpapers || Download paper

  21. 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. (2023). Fu, Yuchen ; Zhang, Xuanrui ; Liu, Jiarui.
    In: Applied Energy.
    RePEc:eee:appene:v:348:y:2023:i:c:s0306261923009510.

    Full description at Econpapers || Download paper

  22. A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting. (2022). Liao, Zhiyuan ; Liu, Peter X ; Wan, Jing ; Huang, Jiehui.
    In: Mathematics.
    RePEc:gam:jmathe:v:10:y:2022:i:11:p:1824-:d:824250.

    Full description at Econpapers || Download paper

  23. Wind Speed Prediction for Offshore Sites Using a Clockwork Recurrent Network. (2022). Shi, Yuxuan ; Wang, Yanyu ; Zheng, Haoran.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:3:p:751-:d:729445.

    Full description at Econpapers || Download paper

  24. A deep asymmetric Laplace neural network for deterministic and probabilistic wind power forecasting. (2022). Wang, Yun ; Zou, Runmin ; Zhang, Lingjun ; Xu, Houhua.
    In: Renewable Energy.
    RePEc:eee:renene:v:196:y:2022:i:c:p:497-517.

    Full description at Econpapers || Download paper

  25. Developing a wind power forecasting system based on deep learning with attention mechanism. (2022). Wei, Wei ; Niu, Tong ; Tian, Chaonan.
    In: Energy.
    RePEc:eee:energy:v:257:y:2022:i:c:s036054422201653x.

    Full description at Econpapers || Download paper

  26. A novel machine learning-based electricity price forecasting model based on optimal model selection strategy. (2022). Hao, Yan ; Yang, Wendong ; Wang, Shouyang ; Sun, Shaolong.
    In: Energy.
    RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221022374.

    Full description at Econpapers || Download paper

  27. Energy storage to solve the diurnal, weekly, and seasonal mismatch and achieve zero-carbon electricity consumption in buildings. (2022). Kuang, Zhonghong ; Liu, Xiaohua ; Zhang, Tao ; Chen, QI.
    In: Applied Energy.
    RePEc:eee:appene:v:312:y:2022:i:c:s0306261922002008.

    Full description at Econpapers || Download paper

Coauthors

Authors registered in RePEc who have wrote about the same topic

Report date: 2025-09-30 11:44:00 || Missing content? Let us know

CitEc is a RePEc service, providing citation data for Economics since 2001. Last updated August, 3 2024. Contact: Jose Manuel Barrueco.