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A multiobjective framework for wind speed prediction interval forecasts

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  • Shrivastava, Nitin Anand
  • Lohia, Kunal
  • Panigrahi, Bijaya Ketan

Abstract

Wind energy is rapidly emerging as a potential and viable replacement for fossil fuels owing to its clean way of power production. However, integration of this abundantly available renewable energy into the power system is constrained by its intermittent nature and unpredictability. Efforts to improve the prediction accuracy of wind speed is therefore imperative for its successful integration into the grid. The uncertainty associated with the prediction is also an important information needed by the system operators for reliable and economic operations. This paper presents the implementation of a multi-objective differential evolution (MODE) algorithm for generation of prediction intervals (PIs) for capturing the uncertainty related to forecasts. Support vector machine (SVM) is used as the machine learning technique and its parameters are tuned such that multiple contradictory objectives are satisfied to generate Pareto-optimal solutions. Several case studies are performed for data from wind farms located in the eastern region of United States. The obtained results prove the successful implementation of the methodology and generation of high quality PIs.

Suggested Citation

  • Shrivastava, Nitin Anand & Lohia, Kunal & Panigrahi, Bijaya Ketan, 2016. "A multiobjective framework for wind speed prediction interval forecasts," Renewable Energy, Elsevier, vol. 87(P2), pages 903-910.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p2:p:903-910
    DOI: 10.1016/j.renene.2015.08.038
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    15. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
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    17. Li, Chaoshun & Tang, Geng & Xue, Xiaoming & Chen, Xinbiao & Wang, Ruoheng & Zhang, Chu, 2020. "The short-term interval prediction of wind power using the deep learning model with gradient descend optimization," Renewable Energy, Elsevier, vol. 155(C), pages 197-211.
    18. Wang, Yun & Hu, Qinghua & Meng, Deyu & Zhu, Pengfei, 2017. "Deterministic and probabilistic wind power forecasting using a variational Bayesian-based adaptive robust multi-kernel regression model," Applied Energy, Elsevier, vol. 208(C), pages 1097-1112.
    19. Shi, Jinhao & Wang, Bo & Luo, Kaiyi & Wu, Yifei & Zhou, Min & Watada, Junzo, 2023. "Ultra-short-term wind power interval prediction based on multi-task learning and generative critic networks," Energy, Elsevier, vol. 272(C).
    20. Honghai Niu & Yu Yang & Lingchao Zeng & Yiguo Li, 2021. "ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power," Energies, MDPI, vol. 14(3), pages 1-15, January.
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    22. Jianzhou Wang & Chunying Wu & Tong Niu, 2019. "A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network," Sustainability, MDPI, vol. 11(2), pages 1-34, January.

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