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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (3): 594-600    DOI: 10.3785/j.issn.1008-973X.2021.03.021
    
Short-term forecasting method of wind power generation based on BP neural network with combined loss function
Fang LIU1(),Zhen WANG1,*(),Rui-di LIU1,Kai WANG2
1. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
2. Department of Computer Science, University of Chinese Academy of Science, Beijing 100049, China
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Abstract  

A short-term forecasting model of neural network for wind power generation with the combined loss function was proposed, in order to reduce the side effect of large-scale wind power integration on power system energy balance and increase system’s wind power accommodation ability. A classification method was introduced into the model, and a BP neural network short-term wind power prediction model with the goal of minimizing the combined loss function was proposed, in order to improve the utilization of raw data information. The combined loss function was constructed by the mean square error loss function, the cross-entropy loss function and the rank loss function according to different weight ratios. Compare to the prediction method based on separate loss functions, the combined loss function proposed can effectively improve the prediction accuracy from real wind farm data test.

Key words： wind power forecast      artificial neural network      power segment      feature extraction      loss function     
Received: 28 February 2020      Published: 25 April 2021
CLC:  TM 614  
Fund:  国家重点研发计划（2017YFB0902000）；国家电网公司科技项目（SGXJ0000KXJS1700841）
Corresponding Authors: Zhen WANG     E-mail: ee_lf@zju.edu.cn;eezwang@ieee.org
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Fang LIU
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Cite this article:

Fang LIU,Zhen WANG,Rui-di LIU,Kai WANG. Short-term forecasting method of wind power generation based on BP neural network with combined loss function. Journal of ZheJiang University (Engineering Science), 2021, 55(3): 594-600.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.03.021     OR     http://www.zjujournals.com/eng/Y2021/V55/I3/594


基于组合损失函数的BP神经网络风力发电短期预测方法

为了改善风电大规模并网带来的电力系统功率平衡问题，提高系统的风电消纳能力，构建了基于组合损失函数的风电功率预测神经网络模型. 为了提高原始数据信息的利用率，在模型中将数据进行分类，提出以最小化组合损失函数为目标的BP神经网络风力发电短期预测模型，由均方差损失函数、交叉熵损失函数和排序损失函数按照不同的权重比构成组合损失函数. 基于实际风场数据，对基于组合损失函数的预测模型效果进行训练和仿真验证，结果表明相较于基于单一的均方差损失函数的预测方法，提出的组合损失函数可有效提高预测精度.


关键词： 风电预测,  人工神经网络,  功率分段,  特征提取,  损失函数 
Fig.1 Schematic diagram of wind power prediction model based on BP-ANN and combined loss function
风电功率/kW 类别 样本数
<50 1 2094
50~110 2 2120
110~180 3 2008
180~290 4 2356
290~500 5 2169
>500 6 2165
Tab.1 List of wind power，category，and sample numbers
Fig.2 Illustration of Softmax layer
Fig.3 Schematic diagram of rank loss function
Fig.4 Actual power curve and the predicted power curve obtained by using the BP-ANN model and proposed model
训练集样本数 模型 $ {{{\varepsilon}} _{{\bf{MAE}}}} $ /kW $ {{{\varepsilon}} _{{\bf{RMSE}}}} $ /kW 训练时间/s
900 BP-ANN 131.9 150.5 1.8
LSTM 154.0 179.1 5
本文 110.0 133.9 3.1
1800 BP-ANN 126.2 144.4 3.0
LSTM 222.9 259.7 16.5
本文 131.7 141.6 4.5
4500 BP-ANN 152.6 186.8 5.4
LSTM 120.9 147.5 26.6
本文 118.1 144.1 8.8
7200 BP-ANN 133.3 185.2 8.3
LSTM 131.6 161.5 42.9
本文 94.5 105.2 13.8
9000 BP-ANN 125.9 187.7 10.2
LSTM 109.2 130.9 78.8
本文 103.2 128.9 16.6
Tab.2 Comparison of prediction performance
训练集样本数 ${{\varepsilon } }_{{{\rm{MAE}}} }$/ ${{\varepsilon } }_{{{\rm{RMSE}}} }$
MSE MSE+CE MSE+CE+RK
900 121.7/145.0 119.5/138.3 110.0/133.9
1800 153.9/162.6 146.1/155.1 131.7/141.6
4500 134.3/161.0 128.5/150.9 118.1/144.1
7200 104.2/113.3 98.5/108.7 94.5/105.2
9000 146.6/171.9 134.0/159.3 103.2/128.9
Tab.3 Comparison of prediction errors of trained models 单位：kW
${{\alpha } }$/ ${{\beta } }$/ ${{\gamma } }$ ${{\varepsilon } }_{{{\rm{RMSE}}} }$/kW ${{\alpha } }$/ ${{\beta } }$/ ${{\gamma } }$ ${{\varepsilon } }_{{{\rm{RMSE}}} }$/kW ${{\alpha } }$/ ${{\beta } }$/ ${{\gamma } }$ ${{\varepsilon } }_{{{\rm{RMSE}}} }$/kW
1/0.5/0.001 138.9 1/1/0.0005 144.1 1/1/0.005 142.3
1/1/0.002 144.2 1/1/0.01 126.4 1/5/0.001 148.8
1/10/0.001 133.9 2/1/0.001 130.5 10/1/0.001 166.7
Tab.4 Comparison of prediction errors of different loss function weight coefficients
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