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Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization

Author

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  • Pavan Kumar Singh

    (MNNIT, Allahabad, India)

  • Nitin Singh

    (MNNIT, Allahabad, India)

  • Richa Negi

    (MNNIT, Allahabad, India)

Abstract

With the upsurge in restructuring of the power markets, wind power has become one of the key factors in power generation in the smart grids and gained momentum in the recent years. The accurate wind power forecasting is highly desirable for reduction of the reserve capability, enhancement in penetration of the wind power, stability and economic operation of the power system. The time series models are extensively used for the wind power forecasting. The model estimation in the ARIMA model is usually accomplished by maximizing the log likelihood function and it requires to be re-estimated for any change in input value. This degrades the performance of the ARIMA model. In the proposed work, the model estimation of the ARIMA model is done using latest evolutionary algorithm (i.e., dynamic particle swarm optimization [DPSO]). The use of DPSO algorithm eliminates the need for re-estimation of the model coefficients for any change in input value and moreover, it improves the performance of ARIMA model. The performance of proposed DPSO-ARIMA model has been compared to the existing models.

Suggested Citation

  • Pavan Kumar Singh & Nitin Singh & Richa Negi, 2021. "Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(2), pages 111-138, April.
    • Handle: RePEc:igg:jcini0:v:15:y:2021:i:2:p:111-138
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    References listed on IDEAS

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    1. Patwal, Rituraj Singh & Narang, Nitin & Garg, Harish, 2018. "A novel TVAC-PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units," Energy, Elsevier, vol. 142(C), pages 822-837.
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    Cited by:

    1. Minhui Qian & Ning Chen & Yuge Chen & Changming Chen & Weiqiang Qiu & Dawei Zhao & Zhenzhi Lin, 2021. "Optimal Coordinated Dispatching Strategy of Multi-Sources Power System with Wind, Hydro and Thermal Power Based on CVaR in Typhoon Environment," Energies, MDPI, vol. 14(13), pages 1-35, June.
    2. Zhenhua Xiong & Yan Chen & Guihua Ban & Yixin Zhuo & Kui Huang, 2022. "A Hybrid Algorithm for Short-Term Wind Power Prediction," Energies, MDPI, vol. 15(19), pages 1-11, October.

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