Research on an Ultra-Short-Term Wind Speed Prediction Method Based on Improved Combined Neural Networks

doi:10.12096/j.2096-4528.pgt.22038

Abstract

Abstract:

Ultra-short-term wind speed prediction is the key to ensure the implementation effect of wind turbine pitch angle feedforward control, and has an important impact on improving the environmental adaptability of wind turbines. In order to improve the prediction accuracy, an ultra-short-term wind speed prediction method based on an improved combined neural networks was proposed. In this method, BP neural network and long short-term memory (LSTM) neural network, which are suitable for time series prediction and have strong nonlinear learning ability, are selected for weighted combination to eliminate the large error that may exist in a single neural network. At the same time, to improve the combination effect, the differential evolution (DE) algorithm was used to optimize the combination weight. The method was applied to the ultra-short-term wind speed prediction of a wind farm. Compared with the results of single neural network prediction and equal weight combined neural networks prediction, the effectiveness of the proposed method in improving the prediction accuracy was verified.

Key words: wind power, ultra-short-term wind speed prediction, BP neural network, long short-term memory (LSTM) neural network, differential evolution (DE) algorithm

CLC Number:

TK 81

Yixiang SHAO, Jian LIU, Liping HU, Liang GUO, Yuan FANG, Rui LI. Research on an Ultra-Short-Term Wind Speed Prediction Method Based on Improved Combined Neural Networks[J]. Power Generation Technology, 2024, 45(2): 323-330.

Figures/Tables 11

Fig. 1 Ultra-short-term wind speed prediction model based on BP neural network

Fig. 2 Ultra-short-term wind speed prediction model based on LSTM neural network

Fig. 3 Schematic diagram of combined neural network ultra-short-term wind speed forecasting based on DE optimization

Fig. 4 15 minute wind speed sequence of a wind farm

Tab. 1 Wind speed sample mapping structure

a个输入	b个输出
x₁，x₂，…，x_a	x_a+₁，x_a+₂，…，x_a+b
x₂，x₃，…，x_a+₁	x_a+₂，x_a+₃，…，x_a+b+₁
…	…
x_K，x_K+₁，…，x_K+a-₁	x_K+a，x_K+a+₂，…，x_K+a+b-₁

Fig. 5 Ultra-short-term wind speed prediction results of the two models

Fig. 6 Frequency histogram of the error distribution of the wind speed prediction results of the two models and its Gauss fitting curve

Tab. 2 Evaluation metrics for the wind speed prediction results of the two models

误差	等权重组合预测模型	DE优化组合预测模型
E_MAE/(m/s)	0.087 7	0.086 1
E_RMSE/(m/s)	0.120 1	0.117 8
E_MAPE/%	1.619 3	1.591 1
E_ME/(m/s)	-0.025 2	-0.025 1

Fig. 7 Ultra-short-term wind speed prediction results of the different models

Fig. 8 Frequency histogram of the error distribution of the wind speed prediction results of the different models and its Gauss fitting curve

Tab. 3 Evaluation metrics for the wind speed prediction results of the different models

误差	BP 神经网络预测模型	LSTM神经网络预测模型	DE优化组合预测模型
E_MAE/(m/s)	0.096 5	0.089 6	0.086 1
E_RMSE/(m/s)	0.129 5	0.122 0	0.117 8
E_MAPE/%	1.772 3	1.652 0	1.591 1
E_ME/(m/s)	-0.025 5	-0.025 3	-0.025 1

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