A Multi-channel Feature Combination Model for Ultra-short-term Wind Power Prediction Under Carbon Neutral Background

doi:10.12096/j.2096-4528.pgt.20103

Abstract

Abstract:

Wind power will become one of the dominant power sources of China oriented to carbon neutral. With the rapid development of artificial intelligence technology, artificial neural networks are widely used in wind power generation forecasting. Traditional artificial neural network algorithms use fixed-form data sets and simple network structures, which limits the overall expression ability and results in uncontrollable errors in ultra-short-term wind power forecasting due to various uncertain factors. In this work, a multi-channel feature combination model based on artificial neural network for ultra-short-term wind power prediction was proposed. Firstly, the data were reclassified and input into three neural networks to establish three feature combinations. After that, multi-channel features splicing and fusion were performed. The fused features were added to the fully connected neural network for power prediction, which can eliminate the interference between different features and effectively learn long-term dependent data features. Finally, the algorithm was verified on the actual data of five wind farms. The experimental results show that this method has better prediction accuracy than the single-channel model, and can improve the network stability.

Key words: carbon neutral, wind power generation, ultra-short-term power prediction, artificial neural network, multi-channel

CLC Number:

TK89

Shubang HUANG, Yao CHEN, Yuqing JIN. A Multi-channel Feature Combination Model for Ultra-short-term Wind Power Prediction Under Carbon Neutral Background[J]. Power Generation Technology, 2021, 42(1): 60-68.

Figures/Tables 18

Fig.1 Basic model of convolutional neural network

Fig.2 LSTM network structure

Fig.3 Multi-channel feature combination model

Fig.4 Available data forms for ultra-short-term power prediction

Fig.5 Schematic diagram of dilated convolution

Fig.6 Improved form of multi-channel neural network

Fig.7 Algorithmic flowchart of multi-channel neural network

Fig.8 Structure of multi-channel neural network

Fig.9 Scatter diagram of measured power and wind speed

Fig.10 Comparison of the measured power and wind speed before and after filtering

Tab. 1 Comparison of prediction results of two algorithms in Jiangsu

风电场	误差准确率		平均准确率
风电场	单通道均方根	多通道均方根	单通道调和	多通道调和
江苏A	0.852 1	0.855 3	0.674 4	0.679 8
江苏B	0.814 9	0.820 2	0.640 2	0.643 5

Fig.11 Comparison of the predicted power of two algorithms in Jiangsu

Tab. 2 Comparison of prediction results of two algorithms in Inner Mongolia

风电场	误差准确率		平均准确率
风电场	单通道均方根	多通道均方根	单通道调和	多通道调和
内蒙C	0.753 6	0.764 6	0.530 3	0.533 8

Fig.12 Comparison of the predicted power of two algorithms in Inner Mongolia

Tab. 3 Comparison of prediction results of two algorithms in Yunnan

风电场	误差准确率		平均准确率
风电场	单通道均方根	多通道均方根	单通道调和	多通道调和
云南D	0.856 2	0.860 3	0.774 7	0.775 9

Fig.13 Comparison of the predicted power of two algorithms in Yunnan

Tab. 4 Comparison of prediction results of two algorithms in Gansu

风电场	误差准确率		平均准确率
风电场	单通道均方根	多通道均方根	单通道调和	多通道调和
甘肃E	0.848 2	0.852 2	0.601 5	0.608 9

Fig.14 Comparison of the predicted power of two algorithms in Gansu

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