EN碳中和背景下多通道特征组合超短期风电功率预测
A Multi-channel Feature Combination Model for Ultra-short-term Wind Power Prediction Under Carbon Neutral Background
- 发电技术 2021年42卷第1期 页码：60-68
- 作者机构：
1.新疆金风科技股份有限公司, 北京市 大兴区 100176
2.江苏金风软件技术有限公司, 江苏省 无锡市 214000
3.河海大学能源与电气学院, 江苏省 南京市 211100
- 作者简介：
[ "黄树帮(1979)，男，硕士，高级工程师，研究方向为智能变电站及新能源发电并网技术，huangshubang@goldwind.com.cn" ]
[ "陈耀(1990)，男，硕士，工程师，研究方向为人工智能与功率预测，yao_chen@aliyun.com" ]
金宇清(1980)，男，博士，副教授，研究方向为新能源发电系统的建模与控制，本文通信作者，jyq16@hhu.edu.cn
- 基金信息：国家自然科学基金项目(52077059)
DOI：10.12096/j.2096-4528.pgt.20103
中图分类号： TK89收稿：2020-10-09，
纸质出版：2021-02-28
- 稿件说明：
移动端阅览
黄树帮, 陈耀, 金宇清. 碳中和背景下多通道特征组合超短期风电功率预测[J]. 发电技术, 2021,42(1):60-68.
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.
黄树帮, 陈耀, 金宇清. 碳中和背景下多通道特征组合超短期风电功率预测[J]. 发电技术, 2021,42(1):60-68. DOI： 10.12096/j.2096-4528.pgt.20103.
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. DOI： 10.12096/j.2096-4528.pgt.20103.
摘要
碳中和背景下，风电将成为我国的主导能源之一。随着人工智能技术快速发展，人工神经网络被广泛应用于风力发电功率预测。传统的人工神经网络算法采用固定形式数据集和单一网络结构，限制了整体表达能力，导致超短期风电功率预测由于各种不确定因素造成难以控制的误差。为此，提出一种基于人工神经网络的多通道特征组合模型，用于超短期风电功率预测。首先将数据集进行重新分类，分别输入到3个神经网络，建立3种特征组合形式；再将多通道特征进行拼接融合，并将融合后的特征加入到全连接神经网络中进行功率预测，可消除不同特征之间的干扰，有效学习到长期依赖的数据特征；最后对5个风电场数据进行算法验证。实验结果表明，该方法比单通道模型能够获得更好的预测精度，而且增加了网络稳定性。
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.
关键词
Keywords
references
全球风能理事会. 全球风能报告[R]. 全球风能理事会, 2019.
Global Wind Energy Council.Global wind energy report[R].Global Wind Energy Council, 2019.
钱峰, 陈艺, 刘俊磊, 等. 大规模风电接入的电力系统协调控制策略[J]. 广东电力, 2019, 32(11): 12-18.
QIAN F, CHEN Y, LIU J L, et al. Coordinated control strategy for power systems with large-scale wind power integration[J]. Guangdong Electric Power, 2019, 32(11): 12-18.
施涛, 李春来, 朱慧敏. 考虑功率预测不确定性的风电消纳随机调度[J]. 电网与清洁能源, 2019, 35(4): 55-59.
SHI T, LI C L, ZHU H M. Stochastic dispatching of wind power accommodation considering uncertaintyof power prediction[J]. Power System and Clean Energy, 2019, 35(4): 55-59.
张坤, 张金环, 张巍巍, 等. 基于ASD-KDE的风电出力超短期区间预测[J]. 智慧电力, 2019, 47(5): 32-37.
ZHANG K, ZHANG J H, ZHANG W W, et al. Ultra-short-term wind power interval prediction based on ASD-KDE[J]. Smart Power, 2019, 47(5): 32-37.
张金环, 王超群, 张彤, 等. 基于高斯混合分布模型的风电功率预测误差统计分析研究[J]. 智慧电力, 2020, 48(7): 59-64.
ZHANG J H, WANG C Q, ZHANG T, et al. Statistical analysis of wind power forecasting errors based on Gaussian mixture model[J]. Smart Power, 2020, 48(7): 59-64.
赵滨滨, 王莹, 王彬, 等. 基于ARIMA时间序列的分布式光伏系统输出功率预测方法研究[J]. 可再生能源, 2019, 37(6): 820-823.
ZHAO B B, WANG Y, WANG B, et al. Photovoltaic power prediction in distribution network based on ARIMA model time series[J]. Renewable Energy Resources, 2019, 37(6): 820-823.
田波, 朴在林, 王慧. 基于时间序列建模在风力发电功率短期预测中的研究[J]. 电网与清洁能源, 2016, 32(3): 115-119.
TIAN B, PIAO Z L, WANG H. Short term prediction of wind power based on time series modeling[J]. Power System and Clean Energy, 2016, 32(3): 115-119.
朱红路, 李旭, 姚建曦, 等. 基于小波分析与神经网络的光伏电站功率预测方法[J]. 太阳能学报, 2015, 36(11): 2725-2730.
ZHU H L, LI X, YAO J X, et al. The power prediction method for photovoltaic power station based on waveletanalysis and neural networks[J]. Acta Energiae Solaris Sinica, 2015, 36(11): 2725-2730.
潘志刚, 刘三明, 李莹, 等. 基于深度学习网络的风电场功率短期预测研究[J]. 科技与创新, 2015(19): 4-6.
PAN Z G, LIU S M, LI Y, et al. Research on the short-term forecasting of wind power based on deep learning network[J]. Science and Technology&Innovation, 2015(19): 4-6.
叶興, 薛家祥. 改进型LSTM网络光伏发电功率预测研究[J]. 中国测试, 2019, 45(11): 14-20.
YE Y, XUE J X. Research on photovoltaic power generation prediction based on improved LSTM network[J]. China Measurement&Testing Technology, 2019, 45(11): 14-20.
王瑞, 刘明山, 周原. 一种基于DBN和卡尔曼滤波算法的AGV定位算法:
CN201710027637.3[P].2017-05-17. WANG R, LIU M S, ZHOU Y.An AGV location algorithm based on DBN and Kalman filter algorithm: CN201710027637.3[P].2017-05-17.
赵怡茗. 基于VMD-SE-LSSVM和迭代误差修正的光伏发电功率预测[D]. 西安: 西安理工大学, 2018.
ZHAO Y M.PV power prediction based on VMD-SE- LSSVM and iterative error correction[D].Xi'an: Xi'an University of Technology, 2018.
CHANG C C, LIN C J. LIBSVM: a library for support vector machines[J]. ACM Transactions on Intelligent Systems&Technology, 2011, 2(3): 1-27.
杨延西, 刘丁. 基于小波变换和最小二乘支持向量机的短期电力负荷预测[J]. 电网技术, 2005, 29(13): 60-64.
YANG Y X, LIU D. Short-term load forecasting based on wavelet transform and least square support vector machines[J]. Power System Technology, 2005, 29(13): 60-64.
周志华. 机器学习[J]. 中国民商, 2016, 3(21): 93.
ZHOU Z H. Machine learning[J]. China's civil and commercial, 2016, 3(21): 93.
李相俊, 许格健. 基于长短期记忆神经网络的风力发电功率预测方法[J]. 发电技术, 2019, 40(5): 426-433.
LI X J, XU G J. Wind power prediction method based on long short-term memory neural network[J]. Power Generation Technology, 2019, 40(5): 426-433.
郎伟明, 麻向津, 周博文, 等. 基于LSTM和非参数核密度估计的风电功率概率区间预测[J]. 智慧电力, 2020, 48(2): 31-37.
LANG W M, MA X J, ZHOU B W, et al. Wind power probabilistic intervals prediction based on LSTM and nonparametric kernel density estimation[J]. Smart Power, 2020, 48(2): 31-37.
周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6): 1229-1251.
ZHOU F Y, JIN L P, DONG J. Review of convolutional neural network[J]. Chinese Journal of Computers, 2017, 40(6): 1229-1251.
牛哲文, 余泽远, 李波, 等. 基于深度门控循环单元神经网络的短期风功率预测模型[J]. 电力自动化设备, 2018, 38(5): 43-49.
NIU Z E, YU Z Y, LI B, et al. Short-term wind power forecasting model based on deep gated recurrent unit neural network[J]. Electric Power Automation Equipment, 2018, 38(5): 43-49.
AHMED A, KHALID M. A review on the selected applications of forecasting models in renewable power systems[J]. Renewable and Sustainable Energy Reviews, 2019, 100: 9-21.
杨正瓴, 冯勇, 熊定方, 等. 基于季风特性改进风电功率预测的研究展望[J]. 智能电网, 2015, 3(1): 1-7.
YANG Z L, FENG Y, XIONG D F. Research prospects of improvement in wind power forecasting based on characteristics of monsoons[J]. Smart Grid, 2015, 3(1): 1-7.
郭丽丽, 丁世飞. 深度学习研究进展[J]. 计算机科学, 2015, 42(5): 28-33.
GUO L L, DING S F. Research progress of deep learning[J]. Computer Science, 2015, 42(5): 28-33.
王星, 李超, 陈吉. 基于膨胀卷积神经网络模型的中文分词方法[J]. 中文信息学报, 2019, 33(9): 24-30.
WANG X, LI C, CHEN J. Dilated convolution neural networks for Chinese word segmentation[J]. Journal of Chinese Information Processing, 2019, 33(9): 24-30.
应稼田, 程良伦, 林锦发. 一种用于风电功率预测的双存储结构LSTM模型[J]. 无线互联科技, 2019, 16(5): 117-120.
YING J T, CHENG L L, LIN J F. A dual storage structure LSTM model for wind power prediction[J]. Wuxian Hulian Keji, 2019, 16(5): 117-120.
刘彬, 戴桂平. 基于白化检验和3σ准则的小波阈值去噪算法[J]. 传感技术学报, 2005, 18(3): 473-476.
LIU B, DAI G P. Adaptive wavelet thresholding denoising algorithm based on white noise detection and 3σ rule[J]. Chinese Journal of Sensors and Actuators, 2005, 18(3): 473-476.
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