Wind power prediction based on BP neural network combined with ERA5 data

doi:10.19799/j.cnki.2095-4239.2024.0603

Abstract

Abstract:

With the continuous development and improvement of wind power generation technology in China, its significance in power system operation and scheduling is becoming increasingly prominent. To predict wind power efficiently and accurately and reduce the negative impact of a large number of wind power on the grid, this paper focuses on a backpropagation (BP) neural network combined with ERA5 data for wind power prediction of a wind farm in the northern part of China and uses particle swarm optimization (PSO) algorithm to optimize the model, combined with the average absolute error, root mean square error and Pearson correlation coefficient to analyze the effect of wind power prediction. The results showed that the predicted and measured wind power changes in the model training set are the same, exhibiting a similar trend of increase and decrease. The average absolute error of the BP model is 702.12 W, with a root mean square error of 1000.18 W and the correlation coefficient is 0.91, whereas the average absolute error of the PSO-BP model is 700.75 W, with a root mean square error of 995.16 W, and the correlation coefficient is 0.94. The ERA5 data in the test set overestimates the wind power to a certain extent; however, the overall trend remains the same. The average absolute error of the BP model is 861.09W, with a root mean square error of 1150.86W, and the correlation coefficient is 0.81, whereas the average absolute error of the PSO-BP model of 829.55W, with a root mean square error of 1117.39 W, and the correlation coefficient is 0.83, indicating effective modeling. The prediction effect of the PSO-BP model is relatively good, and the PSO-BP model has a certain degree of improvement compared with the BP model, which has good applicability in predicting wind power in this region. The results of this study can provide a reference for the prediction of wind power in areas with limited observational data or where the quality of observational data is not high.

Key words: wind power, BP neural network, ERA5 reanalysis information, particle swarm algorithm, wind power prediction

CLC Number:

P4 68

Tingting WANG, Sisheng LI, Wei YU, Fengtian NENG, Xingnan LI, Jialin YANG, Liang XIONG. Wind power prediction based on BP neural network combined with ERA5 data[J]. Energy Storage Science and Technology, 2025, 14(1): 183-189.

Figures/Tables 8

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神经元数量	均方根误差
3	1027.3
4	1016.0
5	1009.3
6	1001.3
7	998.3
8	1011.6
9	1000.3
10	990.3
11	984.9
12	990.9

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