SHORT TERM FORECASTING METHOD OF PHOTOVOLTAICOUTPUT BASED ON DTW-VMD-PSO-BP

Yuan Jianhua; Xie Binbin; He Baolin; Zhao Ziwei; Liu Yu; Liu Bang

doi:10.19912/j.0254-0096.tynxb.2020-1405

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (8) : 58-66. DOI: 10.19912/j.0254-0096.tynxb.2020-1405

SHORT TERM FORECASTING METHOD OF PHOTOVOLTAICOUTPUT BASED ON DTW-VMD-PSO-BP

Yuan Jianhua¹, Xie Binbin¹, He Baolin¹, Zhao Ziwei¹, Liu Yu¹, Liu Bang²

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Abstract

Aiming at the problems of many influencing factors, low prediction accuracy and low stability in short-term prediction of photovoltaic power generation system, a BP neural network photovoltaic power generation prediction method based on particle swarm optimization （PSO） optimization of dynamic time warping （DTW） and variational mode decomposition （VMD） is proposed. Firstly, the dynamic time warping algorithm is used to calculate the data of photovoltaic power generation and influencing factors, to obtain DTW value. Then, according to the DTW value, the radiance which has a greater impact on photovoltaic power generation is selected as the main influencing factor. Next, the variational mode decomposition is used to decompose the influencing factors and photovoltaic power to reduce the volatility and non stationarity of the data, and BP neural network optimized by the particle swarm is used to predict each component Finally, the prediction results are superimposed, to obtain the final prediction value This method is verified in MATLAB by comparison with other neural networks and error analysis. The results comparison show that this method has high accuracy and good stability.

Key words

PV power / particle swarm optimization / neural networks / short-term forecast / dynamic time warping / variational mode decomposition

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Yuan Jianhua, Xie Binbin, He Baolin, Zhao Ziwei, Liu Yu, Liu Bang. SHORT TERM FORECASTING METHOD OF PHOTOVOLTAICOUTPUT BASED ON DTW-VMD-PSO-BP[J]. Acta Energiae Solaris Sinica. 2022, 43(8): 58-66 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1405

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