Wind power forecasting technologies: A review

  • Krishan Kumar Department of Electrical and Electronics Engineering GJUS&T, Hisar 125001, Haryana, India
  • Priti Prabhakar Department of Electrical and Electronics Engineering GJUS&T, Hisar 125001, Haryana, India
  • Avnesh Verma Department of Instrumentation Engineering, Kurukshetra University, Kurukshetra 136119, Haryana, India
Ariticle ID: 538
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Keywords: forecasting; neural networks; pre-processing; time series; wind power forecasting


This study addresses the critical role of wind power forecasting in ensuring stable and reliable power system operations. Wind power forecasting is critical for the efficient operation of plants, time scheduling, and the balancing of power generation with grid integration systems. Due to its dependency on dynamic climatic conditions and associated factors, accurate wind power forecasting is challenging. The research delves into various aspects, including input data, input selection techniques, data pre-processing, and forecasting methods, with the aim of motivating researchers to design highly efficient online/offline models on weather-based data. The overarching goal is to enhance the reliability and stability of power systems while optimizing energy resource utilization. The analysis reveals that hybrid models offer more accurate results, highlighting their significance in the current era. This study investigates different Wind Power Forecasting (WPF) models from existing literature, focusing on input variables, time horizons, climatic conditions, pre-processing techniques, and sample sizes that affect model accuracy. It covers statistical models like ARMA and ARIMA, along with AI techniques including Deep Learning (DL), Machine Learning (ML), and neural networks, to estimate wind power.


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How to Cite
Kumar, K., Prabhakar, P., & Verma, A. (2024). Wind power forecasting technologies: A review. Energy Storage and Conversion, 2(2), 538.