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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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    References listed on IDEAS

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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).
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    3. 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).
    4. 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).
    5. 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).

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