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Home All issues Volume 80 (2025) Sci. Tech. Energ. Transition, 80 (2025) 9 References
Issue
Sci. Tech. Energ. Transition
Volume 80, 2025
Decarbonizing Energy Systems: Smart Grid and Renewable Technologies
Article Number 9
Number of page(s) 12
DOI https://doi.org/10.2516/stet/2024100
Published online 06 January 2025
  • Deng X., Shao H., Hu C., Jiang D., Jiang Y. (2020) Wind power forecasting methods based on deep learning: A survey, Comput. Model. Eng. Sci. 122, 1, 273–302. [Google Scholar]
  • Foley A.M., Leahy P.G., Marvuglia A., McKeogh E.J. (2012) Current methods and advances in forecasting of wind power generation, Renew. Energy 37, 1, 1–8. [CrossRef] [Google Scholar]
  • Wang X., Guo P., Huang X. (2011) A review of wind power forecasting models, Energy Proc. 12, 770–778. [CrossRef] [Google Scholar]
  • 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, Appl. Energy 188, 56–70. [CrossRef] [Google Scholar]
  • Tastu J., Pinson P., Trombe P.J., Madsen H. (2013) Probabilistic forecasts of wind power generation accounting for geographically dispersed information, IEEE Trans. Smart Grid 5, 1, 480–489. [Google Scholar]
  • Chen C., Liu H. (2020) Medium-term wind power forecasting based on multi-resolution multi-learner ensemble and adaptive model selection, Energy Convers. Manag. 206, 112492. [CrossRef] [Google Scholar]
  • Landberg L. (1998) A mathematical look at a physical power prediction model, Wind Energy 1, 1, 23–28. [CrossRef] [Google Scholar]
  • Chen P., Pedersen T., Bak-Jensen B., Chen Z. (2009) ARIMA-based time series model of stochastic wind power generation, IEEE Trans. Power Syst. 25, 2, 667–676. [Google Scholar]
  • Wang Q., Liu W., Yu H., Zheng S., Gao S., Granelli F. (2019) CPAC: Energy-efficient algorithm for IoT sensor networks based on enhanced hybrid intelligent swarm, Comput. Model. Eng. Sci. 121, 1, 83–103. [Google Scholar]
  • Milligan M., Schwartz M., Wan Y.H. (2003) Statistical wind power forecasting models: Results for US wind farms, NREL/CP-500-33956, National Renewable Energy Lab (NREL), Golden, CO (United States). [Google Scholar]
  • Sideratos G., Hatziargyriou N.D. (2007) An advanced statistical method for wind power forecasting, IEEE Trans. Power Syst. 22, 1, 258–265. [CrossRef] [Google Scholar]
  • Yuan X., Tan Q., Lei X., Yuan Y., Wu X. (2017) Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine, Energy 129, 122–137. [CrossRef] [Google Scholar]
  • Adedipe T., Shafiee M., Zio E. (2020) Bayesian network modelling for the wind energy industry: An overview, Reliab. Eng. Syst. Saf. 202, 107053. [CrossRef] [Google Scholar]
  • Sun W., Wang Y. (2018) Short-term wind speed forecasting based on fast ensemble empirical mode decomposition, phase space reconstruction, sample entropy and improved back-propagation neural network, Energy Convers. Manag. 157, 1–12. [CrossRef] [Google Scholar]
  • Hu Q., Zhang S., Yu M., Xie Z. (2015) Short-term wind speed or power forecasting with heteroscedastic support vector regression, IEEE Trans. Sustain. Energy 7, 1, 241–249. [Google Scholar]
  • Zeng J., Qiao W. 2011 Support vector machine-based short-term wind power forecasting, in: 2011 IEEE/PES Power Syst. Conf. Expos., IEEE, pp. 1–8. [Google Scholar]
  • Liu H., Tian H.Q., Li Y.F. (2015) Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms, Energy Convers. Manag. 100, 16–22. [CrossRef] [Google Scholar]
  • Hong Y.Y., Rioflorido C.L.P.P. (2019) A hybrid deep learning-based neural network for 24-h ahead wind power forecasting, Appl. Energy 250, 530–539. [CrossRef] [Google Scholar]
  • Liu X., Zhou J. (2024) Short-term wind power forecasting based on multivariate/multi-step LSTM with temporal feature attention mechanism, Appl. Soft Comput. 150, 111050. [CrossRef] [Google Scholar]
  • Chen J., Zeng G.Q., Zhou W., Du W., Lu K.D. (2018) Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization, Energy Convers. Manag. 165, 681–695. [CrossRef] [Google Scholar]
  • Yildiz C., Acikgoz H., Korkmaz D., Budak U. (2021) An improved residual-based convolutional neural network for very short-term wind power forecasting, Energy Convers. Manag. 228, 113731. [CrossRef] [Google Scholar]
  • Yin H., Ou Z., Huang S., Meng A. (2019) A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition, Energy 189, 116316. [CrossRef] [Google Scholar]
  • Liu H., Mi X., Li Y., Duan Z., Xu Y. (2019) Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression, Renew. Energy 143, 842–854. [CrossRef] [Google Scholar]
  • Zhao Z., Yun S., Jia L., Guo J., Meng Y., He N., Yang L. (2023) Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features, Eng. Appl. Artif. Intell. 121, 105982. [CrossRef] [Google Scholar]
  • Wu H., Xu J., Wang J., Long M. (2021) Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting, Adv. Neural Inf. Process. Syst. 34, 22419–22430. [Google Scholar]
  • Zhou H., Ren D., Xia H., Fan M., Yang X., Huang H. (2021) Ast-gnn: An attention-based spatio-temporal graph neural network for interaction-aware pedestrian trajectory prediction, Neurocomputing 445, 298–308. [CrossRef] [Google Scholar]
  • Li H. (2022) Short-term wind power prediction via spatial temporal analysis and deep residual networks, Front. Energy Res. 10, 920407. [CrossRef] [Google Scholar]
  • Song Y., Tang D., Yu J., Yu Z., Li X. (2022) Short-term forecasting based on graph convolution networks and multiresolution convolution neural networks for wind power, IEEE Trans. Ind. Inf. 19, 2, 1691–1702. [Google Scholar]
  • Liu Z., Li M., Yang B., Wei H. (2022) Spatial wind power forecasting using a GRU-based model. Baidu KDD Cup, WindTeam CSU123. [Google Scholar]
  • Wang S., Zhang N., Wu L., Wang Y. (2016) Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method, Renew. Energy 94, 629–636. [CrossRef] [Google Scholar]
  • Hamilton W., Ying Z., Leskovec J. (2017) Inductive representation learning on large graphs, Adv. Neural Inf. Process. Syst. 30, 1025–1035. [Google Scholar]
  • Veličković P., Cucurull G., Casanova A., Romero A., Lio P., Bengio Y. (2017) Graph attention networks, Stat 1050, 20, 10–48550. arXiv preprint arXiv:1710.10903. [Google Scholar]
  • Bruna J., Zaremba W., Szlam A., LeCun Y. (2013) Spectral networks and locally connected networks on graphs, arXiv preprint arXiv:1312.6203. [Google Scholar]
  • Defferrard M., Bresson X., Vandergheynst P. (2016) Convolutional neural networks on graphs with fast localized spectral filtering, Adv. Neural Inf. Process. Syst. 29, 3844–3852. [Google Scholar]
  • Khodayar M., Wang J. (2018) Spatio-temporal graph deep neural network for short-term wind speed forecasting, IEEE Trans. Sustain. Energy 10, 2, 670–681. [Google Scholar]
  • Bentsen L.Ø., Warakagoda N.D., Stenbro R., Engelstad P. (2022) May) Wind park power prediction: Attention-based graph networks and deep learning to capture wake losses, In J. Phys.: Conf. Ser 2265, 2, 022035. IOP Publishing. [CrossRef] [Google Scholar]
  • Wu Z., Huang N.E. (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method, Adv. Adapt. Data Anal. 1, 1, 1–41. [CrossRef] [Google Scholar]
  • Li Y., Yu R., Shahabi C., Liu Y. (2018) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting, ICLR-2018. arXiv preprint arXiv:1707.01926. [Google Scholar]
  • Bai L., Yao L., Li C., Wang X., Wang C. (2020) Adaptive graph convolutional recurrent network for traffic forecasting, Adv. Neural Inf. Process. Syst. 33, 17804–17815. [Google Scholar]
  • Zhang Y., Liu L., Xiong X., Li G., Wang G., Lin L. (2023) Long-term wind power forecasting with hierarchical spatial-temporal transformer, IJCAI-2023, 6308–6316. arXiv preprint arXiv:2305.18724. [CrossRef] [Google Scholar]
  • Xu B., Shen H., Cao Q., Qiu Y., Cheng X. (2019) Graph wavelet neural network, ICLR-2019. arXiv preprint arXiv:1904.07785. [Google Scholar]
  • Liu M., Zeng A., Chen M., Xu Z., Lai Q., Ma L., Xu Q. (2022) Scinet: Time series modeling and forecasting with sample convolution and interaction, Adv. Neural Inf. Process. Syst. 35, 5816–5828. [Google Scholar]
  • Yu Y., Si X., Hu C., Zhang J. (2019) A review of recurrent neural networks: LSTM cells and network architectures, Neural Comput. 31, 7, 1235–1270. [CrossRef] [Google Scholar]
  • De Myttenaere A., Golden B., Le Grand B., Rossi F. (2016) Mean absolute percentage error for regression models, Neurocomputing 192, 38–48. [CrossRef] [Google Scholar]
  • Liu Z.H., Wang C.T., Wei H.L., Zeng B., Li M., Song X.P. (2024) A wavelet-LSTM model for short-term wind power forecasting using wind farm SCADA data, Expert Syst. with Appl. 247, 123237. [CrossRef] [Google Scholar]
  • Yang T., Yang Z., Li F., Wang H. (2024) A short-term wind power forecasting method based on multivariate signal decomposition and variable selection, Appl. Energy 360, 122759. [CrossRef] [Google Scholar]
  • Alanazi M.A., Aloraini M., Islam M., Alyahya S., Khan S. (2023) Wind energy assessment using Weibull distribution with different numerical estimation methods: a case study, Eme. Sci. J. 7, 6, 2260–2278. [CrossRef] [Google Scholar]
  • Bedewy B.A.H., Al-Timimy S.R.A. (2023) Estimate suitable location of solar power plants distribution by GIS spatial analysis, Civil Eng. J. 9, 5, 1217–1229. [CrossRef] [Google Scholar]
  • Balal A.T., Jafarabadi Y.P.T., Demir A.T., Igene M.T., Giesselmann M.T., Bayne S.T. (2023) Forecasting solar power generation utilizing machine learning models in Lubbock, Eme. Sci. J. 7, 4, 1052–1062. [CrossRef] [Google Scholar]
  • Sharma S., Sharma S., Athaiya A. (2017) Activation functions in neural networks, Towards Data Sci. 6, 12, 310–316. [Google Scholar]
  • Van Laarhoven T. (2017) L2 regularization versus batch and weight normalization, arXiv preprint arXiv:1706.05350. [Google Scholar]

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