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Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network

Author

Listed:
  • Chia-Hung Wang

    (College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350011, China
    Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350011, China)

  • Qigen Zhao

    (College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350011, China)

  • Rong Tian

    (College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350011, China)

Abstract

Wind power prediction is an important research topic in the wind power industry and many prediction algorithms have recently been studied for the sake of achieving the goal of improving the accuracy of short-term forecasting in an effective way. To tackle the issue of generating a huge transition matrix in the traditional Markov model, this paper introduces a real-time forecasting method that reduces the required calculation time and memory space without compromising the prediction accuracy of the original model. This method is capable of obtaining the state probability interval distribution for the next moment through real-time calculation while preserving the accuracy of the original model. Furthermore, the proposed Markov-based Back Propagation (BP) neural network was optimized using the Particle Swarm Optimization (PSO) algorithm in order to effectively improve the prediction approach with an improved PSO-BP neural network. Compared with traditional methods, the computing time of our improved algorithm increases linearly, instead of growing exponentially. Additionally, the optimized Markov-based PSO-BP neural network produced a better predictive effect. We observed that the Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) of the prediction model were 12.7% and 179.26, respectively; compared with the existing methods, this model generates more accurate prediction results.

Suggested Citation

  • Chia-Hung Wang & Qigen Zhao & Rong Tian, 2023. "Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network," Energies, MDPI, vol. 16(11), pages 1-24, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4282-:d:1154052
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    References listed on IDEAS

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    1. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2013. "First and second order semi-Markov chains for wind speed modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1194-1201.
    2. Qinming Liu & Daigao Li & Wenyi Liu & Tangbin Xia & Jiaxiang Li, 2021. "A Novel Health Prognosis Method for a Power System Based on a High-Order Hidden Semi-Markov Model," Energies, MDPI, vol. 14(24), pages 1-19, December.
    3. Pingping Yun & Yongfeng Ren & Yu Xue, 2018. "Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method," Energies, MDPI, vol. 11(12), pages 1-23, December.
    4. Larissa Batrancea & Marcel Ciprian Pop & Malar Maran Rathnaswamy & Ioan Batrancea & Mircea-Iosif Rus, 2021. "An Empirical Investigation on the Transition Process toward a Green Economy," Sustainability, MDPI, vol. 13(23), pages 1-12, November.
    5. Shamshad, A. & Bawadi, M.A. & Wan Hussin, W.M.A. & Majid, T.A. & Sanusi, S.A.M., 2005. "First and second order Markov chain models for synthetic generation of wind speed time series," Energy, Elsevier, vol. 30(5), pages 693-708.
    6. Li, Wenzhe & Jia, Xiaodong & Li, Xiang & Wang, Yinglu & Lee, Jay, 2021. "A Markov model for short term wind speed prediction by integrating the wind acceleration information," Renewable Energy, Elsevier, vol. 164(C), pages 242-253.
    7. Nfaoui, H. & Essiarab, H. & Sayigh, A.A.M., 2004. "A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco," Renewable Energy, Elsevier, vol. 29(8), pages 1407-1418.
    8. Shuai Zhang & Yongxiang Zhang & Jieping Zhu, 2018. "Residual life prediction based on dynamic weighted Markov model and particle filtering," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 753-761, April.
    9. Chia-Hung Wang & Shumeng Chen & Qigen Zhao & Yifan Suo, 2023. "An Efficient End-to-End Obstacle Avoidance Path Planning Algorithm for Intelligent Vehicles Based on Improved Whale Optimization Algorithm," Mathematics, MDPI, vol. 11(8), pages 1-31, April.
    10. Marta Poncela-Blanco & Pilar Poncela, 2021. "Improving Wind Power Forecasts: Combination through Multivariate Dimension Reduction Techniques," Energies, MDPI, vol. 14(5), pages 1-16, March.
    11. Gustavo A. Nunez Segura & Cintia Borges Margi, 2021. "Centralized Energy Prediction in Wireless Sensor Networks Leveraged by Software-Defined Networking," Energies, MDPI, vol. 14(17), pages 1-18, August.
    12. Munkhammar, Joakim & van der Meer, Dennis & Widén, Joakim, 2021. "Very short term load forecasting of residential electricity consumption using the Markov-chain mixture distribution (MCM) model," Applied Energy, Elsevier, vol. 282(PA).
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