Wind Speed Prediction Model Based on Improved VMD and Sudden Change of Wind Speed

Shijun Wang, Chun Liu, Kui Liang, Ziyun Cheng, Xue Kong and Shuang Gao
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Shijun Wang: Division of Development, Gansu Electric Power Corporation, State Grid (Research Institute of Economy and Technology), Lanzhou 730050, China
Chun Liu: Division of Development, Gansu Electric Power Corporation, State Grid (Research Institute of Economy and Technology), Lanzhou 730050, China
Kui Liang: Division of Development, Gansu Electric Power Corporation, State Grid (Research Institute of Economy and Technology), Lanzhou 730050, China
Ziyun Cheng: Division of Development, Gansu Electric Power Corporation, State Grid (Research Institute of Economy and Technology), Lanzhou 730050, China
Xue Kong: Department of Mathematics and Physics, North China Electric Power University, Baoding 071003, China
Shuang Gao: School of Mathematical Sciences, Hebei Normal University, Shijiazhuang 050023, China

Sustainability, 2022, vol. 14, issue 14, 1-15

Abstract: An accurate wind speed prediction system is of great importance prerequisite for realizing wind power grid integration and ensuring the safety of the power system. Quantifying wind speed fluctuations can better provide valuable information for power dispatching. Therefore, this paper proposes a deterministic wind speed prediction system and an interval prediction method based on the Lorentzian disturbance sequence. For deterministic forecasting, a variational modal decomposition algorithm is first used to reduce noise. The preprocessed data are then predicted by a long and short-term neural network, but there is a significant one-step lag in the results. In response to such limitation, a wind speed slope is introduced to revise the preliminary prediction results, and the final deterministic wind speed prediction model is obtained. For interval prediction, on the basis of deterministic prediction, the Lorenz disturbance theory is introduced to describe the dynamic atmospheric system. B-spline interpolation is used to fit the distribution of Lorenz disturbance theory series to obtain interval prediction results. The experimental results show that the model proposed in this paper can achieve higher forecasting accuracy than the benchmark model, and the interval prediction based on the Lorentzian disturbance sequence can achieve a higher ground truth coverage rate when the average diameter is small through B-spline interpolation fitting.

Keywords: wind speed forecasting system; interval forecasting; variational mode decomposition (VMD); wind speed ramp (WSR); improved particle swarm optimization (PSOR); Lorentzian system (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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