基于DTW-VMD-PSO-BP的光伏发电功率短期预测方法

袁建华; 谢斌斌; 何宝林; 赵子玮; 刘宇; 刘邦

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

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太阳能学报 ›› 2022, Vol. 43 ›› Issue (8) : 58-66. DOI: 10.19912/j.0254-0096.tynxb.2020-1405

基于DTW-VMD-PSO-BP的光伏发电功率短期预测方法

袁建华¹, 谢斌斌¹, 何宝林¹, 赵子玮¹, 刘宇¹, 刘邦²

作者信息 +

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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文章历史 +

摘要

针对光伏发电系统短期预测影响因素较多、预测精度较低、稳定度不高等问题,提出一种基于动态时间弯曲（DTW）和变分模态分解（VMD）的粒子群（PSO）优化的BP神经网络光伏发电预测方法。首先使用动态时间弯曲算法对光伏发电功率及影响因素的数据进行测算得到DTW值,再根据DTW值选择对光伏发电功率影响较大的辐射度作为主要影响因素,然后利用变分模态分解将影响因素及光伏发电功率进行分解,降低数据的波动性和非平稳性。运用粒子群优化的BP神经网络对各分量进行预测,然后将预测结果进行叠加,叠加所得结果即为最后预测结果。在Matlab中对该方法和其他神经网络进行算例验证和误差分析,结果表明采用该方法预测结果精度高,稳定性好。

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.

导出引用

袁建华, 谢斌斌, 何宝林, 赵子玮, 刘宇, 刘邦. 基于DTW-VMD-PSO-BP的光伏发电功率短期预测方法[J]. 太阳能学报. 2022, 43(8): 58-66 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1405

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

中图分类号： TM615

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