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Variable frequency wind speed trend prediction system based on combined neural network and improved multi-objective optimization algorithm

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  • Tian, Zhirui
  • Wang, Jiyang

Abstract

Trend prediction data with low measurement frequency has always been needed in wind power station, but the traditional multi-step prediction method has caused error accumulation and led to poor prediction accuracy, in order to solve this problem, a new wind speed trend prediction system is proposed which includes data preprocessing (Fuzzy Information Granulation), combined neural network prediction and an improved multi-objective manta rays foraging optimization based on Tent chaotic map and T-distribution perturbation operator (IMOMRFO). The algorithm not only has a good ability to escape from the local optimal solution, but also proves theoretically that the Pareto optimal solution is obtained. Through the simulation of four groups of experiments, it is obvious that the stability, generalization and accuracy of the model are satisfactory. It is confirmed that the model greatly improves the accuracy of trend prediction and makes a certain contribution to solve the problem of wind speed prediction, through the test of the ability of point prediction and interval prediction of the model.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:254:y:2022:i:pa:s0360544222011525
    DOI: 10.1016/j.energy.2022.124249
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    1. Hofer, Roswitha, 2018. "Halton-type sequences in rational bases in the ring of rational integers and in the ring of polynomials over a finite field," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 143(C), pages 78-88.
    2. Hu, Weicheng & Yang, Qingshan & Chen, Hua-Peng & Yuan, Ziting & Li, Chen & Shao, Shuai & Zhang, Jian, 2021. "New hybrid approach for short-term wind speed predictions based on preprocessing algorithm and optimization theory," Renewable Energy, Elsevier, vol. 179(C), pages 2174-2186.
    3. Tonn, B. & Rose, E. & Hawkins, B., 2018. "Evaluation of the U.S. department of energy’s weatherization assistance program: Impact results," Energy Policy, Elsevier, vol. 118(C), pages 279-290.
    4. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).
    5. Choe, Do-Eun & Kim, Hyoung-Chul & Kim, Moo-Hyun, 2021. "Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades," Renewable Energy, Elsevier, vol. 174(C), pages 218-235.
    6. Sanders, Dwight R. & Manfredo, Mark R. & Boris, Keith, 2008. "Accuracy and efficiency in the U.S. Department of Energy's short-term supply forecasts," Energy Economics, Elsevier, vol. 30(3), pages 1192-1207, May.
    7. Niu, Xinsong & Wang, Jiyang, 2019. "A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 241(C), pages 519-539.
    8. Faure, Henri & Lemieux, Christiane, 2019. "Implementation of irreducible Sobol’ sequences in prime power bases," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 161(C), pages 13-22.
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    Cited by:

    1. Tian, Zhirui & Gai, Mei, 2023. "A novel hybrid wind speed prediction framework based on multi-strategy improved optimizer and new data pre-processing system with feedback mechanism," Energy, Elsevier, vol. 281(C).
    2. Tian, Zhirui & Wang, Jiyang, 2023. "A wind speed prediction system based on new data preprocessing strategy and improved multi-objective optimizer," Renewable Energy, Elsevier, vol. 215(C).
    3. Wang, Jianing & Zhu, Hongqiu & Zhang, Yingjie & Cheng, Fei & Zhou, Can, 2023. "A novel prediction model for wind power based on improved long short-term memory neural network," Energy, Elsevier, vol. 265(C).
    4. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Yang, Anren & Yan, Yinlian & Pan, Yachao & Wang, Yan, 2023. "Ensemble of self-organizing adaptive maps and dynamic multi-objective optimization for organic Rankine cycle (ORC) under transportation and driving environment," Energy, Elsevier, vol. 275(C).
    5. Suo, Leiming & Peng, Tian & Song, Shihao & Zhang, Chu & Wang, Yuhan & Fu, Yongyan & Nazir, Muhammad Shahzad, 2023. "Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm," Energy, Elsevier, vol. 276(C).

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