计及湍流强度的风电功率短期预测
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摘要
由于风能具有波动性、间歇性和不可控等特点,为了提高风电输出功率的预测精度,提出一种计及湍流强度的时间卷积网络预测方法。为了更好地表征风速波动特征,在气象数据中引入湍流强度变量,同时使用最新的时间卷积网络架构模型提高预测精确度。为验证该预测模型的有效性,对比了输入变量加入湍流强度前后的预测效果,以及采用反向传播神经网络和长短时间记忆神经网络预测的效果。采用实际数据的预测结果表明,所提方法的网络结构简单,提取信息方式直接,记忆区域长短可调,且预测精度较高。
Wind energy has the nature of uncertainty,uncontrollability and intermittency. In order to improve accuracy of wind power prediction, this paper proposes a prediction method based on temporal convolutional network(TCN) considering turbulence intensity. In this method, to better characterize wind speed fluctuation, turbulence intensity is added to meteorological data, and the latest TCN architecture is introduced to improve prediction accuracy. Comparison between the two cases of adding and not adding turbulence intensity is performed to verify validity of the model, and the effects of back propagation(BP) neural network and long short-term memory(LSTM) network prediction are compared. The prediction results with actual data show that the proposed method has the advantages of simple network structure, direct information extraction, adjustable memory length and high prediction accuracy.
Wind energy has the nature of uncertainty,uncontrollability and intermittency. In order to improve accuracy of wind power prediction, this paper proposes a prediction method based on temporal convolutional network(TCN) considering turbulence intensity. In this method, to better characterize wind speed fluctuation, turbulence intensity is added to meteorological data, and the latest TCN architecture is introduced to improve prediction accuracy. Comparison between the two cases of adding and not adding turbulence intensity is performed to verify validity of the model, and the effects of back propagation(BP) neural network and long short-term memory(LSTM) network prediction are compared. The prediction results with actual data show that the proposed method has the advantages of simple network structure, direct information extraction, adjustable memory length and high prediction accuracy.
引文
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[12]黄婷婷,余磊.SDAE-LSTM模型在金融时间序列预测中的应用[J].计算机工程与应用,2019,55(1):142-148.Huang Tingting,Yu Lei.Application of SDAE-LSTM model on financial time series forecasting[J].Computer Engineering and Applications,2019,55(1):142-148(in Chinese).
[13]李鹏,何帅,韩鹏飞,等.基于长短期记忆的实时电价条件下智能电网短期负荷预测[J].电网技术,2018,42(12):4045-4052.Li Peng,He Shuai,Han Pengfei,et al.Short term load forecasting of smart grid based on long short-term memory recurrent neural networks in condition of real-time electricity price[J].Power System Technology,2018,42(12):4045-4052(in Chinese).
[14]杨国田,张涛,王英男,等.基于长短期记忆神经网络的火电厂NO_x排放预测模型[J].热力发电,2018,47(10):12-17.Yang Guotian,Zhang Tao,Wang Yingnan,et al.Prediction model for NO_x emissions from thermal power plants based on long-short-term memory neural network[J].Thermal Power Generation,2018,47(10):12-17(in Chinese).
[15]朱乔木,李弘毅,王子琪,等.基于长短期记忆网络的风电场发电功率超短期预测[J].电网技术,2017,41(12):3797-3802.Zhu Qiaomu,Li Hongyi,Wang Ziqi,et al.Short-term wind power forecasting based on LSTM[J].Power System Technology,2017,41(12):3797-3802(in Chinese).
[16]Zihang Dai,Zhilin Yang,Yiming Yang,et al.Transformer-XL:language modeling with longer-term dependency[C]//International Conference on Learning Representations(ICLR).New Orleans,Louisiana,United States:Google,2019:1-16.
[17]Long Dingkun,Zhang Richong,Mao Yongyi.Recurrent neural networks with finite memory length[J].IEEE Access,2019(7):12511-12520.
[18]Bai S,Kolter J Z,Koltun V.An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[J].arXiv preprint:1803.01271,2018:1-14.
[19]Fisher Yu,Vladlen Koltun.Multi-scale context aggregation by dilated convolutions[C]//International Conference on Learning Representations(ICLR).New Orleans,Puerto Rico:Universitéde Sherbrooke,2016:1-9.
[20]Tim S,Diederik P.Weight normalization:a simple parameterization to accelerate training of deep neural networks[C]//Neural Information Processing Systems(NIPS).Barcelona,Spain:University of Pennsylvania,2016:1-9.
[21]He Kaiming,Zhang Xiangyu,Ren Shaoqing,et al.Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Las Vegas,United States:IEEE,2016:770-778.
[22]韩花丽,张根保,杨微.湍流强度对风电机组测量功率曲线的影响及修正[J].太阳能学报,2015,36(6):1442-1447.Han Huali,Zhagn Genbao,Yang Wei,Influence and adjustment of turbulence intensity on measured power curve of wind turbine[J].Acta Energiae Solaris Sinica,2015,36(6):1442-1447(in Chinese).
[23]Sonia W,Julie K L.Atmospheric stability affects wind turbine power collection[J].Environmental Research Letters,2012,7(1):1-9.
[24]陈燕,马春燕,谭沛然,等.湍流值对风电功率预测的影响与分析[J].电测与仪表,2018,55(20):29-33.Chen Yan,Ma Chunyan,Tan Peiran,et al.Influence and analysis of turbulence value on wind power prediction[J].Electrical Measurement and Instrumentation,2018,55(20):29-33(in Chinese).
[25]Wang Xiaoming,Xie Yugang,Gao Bo,et al.Analysis of factors affecting wind farm output power[C]//2018 2nd IEEE Conference on Energy Internet and Energy System Integration(EI2).Beijing,China:IEEE,2018:1-5.
[26]徐曼,乔颖,鲁宗相.短期风电功率预测误差综合评价方法[J].电力系统自动化,2011,35(12):20-26.Xu Man,Qiao Ying,Lu Zongxiang.A comprehensive error evaluation method for short-term wind power prediction[J].Automation of Electric Power Systems.2011,35(12):20-26(in Chinese).
[2]钱政,裴岩,曹利宵,等.风电功率预测方法综述[J].高电压技术,2016,42(4):1047-1060.Qian Zheng,Pei Yan,Cao Lixiao,et al.Review of wind power forecasting method[J].High Voltage Engineering,2016,42(4):1047-1060(in Chinese).
[3]Jung J,Broadwater R P.Current status and future advances for wind speed and power forecasting[J].Renewable and Sustainable Energy Reviews,2014(31):762-777.
[4]陈昊,高山,王玉荣,等.基于广义自回归条件异方差偏度峰度模型的风电功率预测方法[J].中国电机工程学报,2017,37(12):3456-3461.Chen Hao,Gao Shan,Wang Yurong,et al.Wind power forecasting method based on generalized autoregressive conditional heteroskedasticity with skewness and kurtosis model[J].Proceedings of CSEE,2017,37(12):3456-3461(in Chinese).
[5]周玮,钟佳成,孙辉,等.基于隐马尔可夫模型的日内风电功率预测误差区间滚动估计[J].电力系统自动化,2018,42(21):90-95.Zhou Wei,Zhong Jiacheng,Sun Hui,et al.An interval rolling estimation method for daily wind power forecast errors based on hidden Markov model[J].Automation of Electric Power Systems,2018,42(21):90-95(in Chinese).
[6]Peng L,Lili Y.Short-term wind speed forecasting based on non-stationary time series analysis and ARCH model[C]//Proceedings of the 2011 International Conference on Multimedia Technology(ICMT).Hangzhou,China,2011:2549-2553.
[7]叶瑞丽,郭志忠,刘瑞叶,等.基于小波包分解和改进Elman神经网络的风电场风速和风电功率预测[J].电工技术学报,2017,32(21):103-111.Ye Ruili,Guo Zhizhong,Liu Ruiye,et al.Wind speed and wind power forecasting method based on wavelet packet decomposition and improved Elman neural network[J].Transactions of China Electrotechnical Society,2017,32(21):103-111(in Chinese).
[8]孟安波,卢海明,胡函武,等.混合小波包与纵横交叉算法的风电预测神经网络模型[J].太阳能学报,2015,36(7):1645-1651.Meng Anbo,Lu Haiming,Hu Hanwu,et al.Hybrid wavelet PACHET-CSO-ENN approach for short-term wind power forecasting[J].ACTA Energiae Solaris Sinica,2015,36(7):1645-1651(in Chinese).
[9]范高锋,王伟胜,刘纯.基于人工神经网络的风电功率短期预测系统[J].电网技术,2008,32(22):72-76.Fan Gaofeng,Wang Weisheng,Liu Chun.Artificial neural network based wind power short term prediction system[J].Power System Technology,2008,32(22):72-76(in Chinese).
[10]曾鸣,李树雷,王良,等.基于ARMA模型和BP神经网络组合优化算法的风电预测模型[J].华东电力,2013,41(2):347-352.Zeng Ming,Li Shulei,Wang Liang,et al.Wind power prediction model based on the combined optimization algorithm of ARMAmodel and BP neural networks[J].East China Electric Power,2013,41(2):347-352(in Chinese).
[11]周松林,茆美琴,苏建徽.基于主成分分析与人工神经网络的风电功率预测[J].电网技术,2011,35(9):128-132.Zhou Songlin,Mao Meiqin,Su Jianhui.Prediction of wind power based on principal component analysis and artificial neural network[J].Power System Technology,2011,35(9):128-132(in Chinese).
[12]黄婷婷,余磊.SDAE-LSTM模型在金融时间序列预测中的应用[J].计算机工程与应用,2019,55(1):142-148.Huang Tingting,Yu Lei.Application of SDAE-LSTM model on financial time series forecasting[J].Computer Engineering and Applications,2019,55(1):142-148(in Chinese).
[13]李鹏,何帅,韩鹏飞,等.基于长短期记忆的实时电价条件下智能电网短期负荷预测[J].电网技术,2018,42(12):4045-4052.Li Peng,He Shuai,Han Pengfei,et al.Short term load forecasting of smart grid based on long short-term memory recurrent neural networks in condition of real-time electricity price[J].Power System Technology,2018,42(12):4045-4052(in Chinese).
[14]杨国田,张涛,王英男,等.基于长短期记忆神经网络的火电厂NO_x排放预测模型[J].热力发电,2018,47(10):12-17.Yang Guotian,Zhang Tao,Wang Yingnan,et al.Prediction model for NO_x emissions from thermal power plants based on long-short-term memory neural network[J].Thermal Power Generation,2018,47(10):12-17(in Chinese).
[15]朱乔木,李弘毅,王子琪,等.基于长短期记忆网络的风电场发电功率超短期预测[J].电网技术,2017,41(12):3797-3802.Zhu Qiaomu,Li Hongyi,Wang Ziqi,et al.Short-term wind power forecasting based on LSTM[J].Power System Technology,2017,41(12):3797-3802(in Chinese).
[16]Zihang Dai,Zhilin Yang,Yiming Yang,et al.Transformer-XL:language modeling with longer-term dependency[C]//International Conference on Learning Representations(ICLR).New Orleans,Louisiana,United States:Google,2019:1-16.
[17]Long Dingkun,Zhang Richong,Mao Yongyi.Recurrent neural networks with finite memory length[J].IEEE Access,2019(7):12511-12520.
[18]Bai S,Kolter J Z,Koltun V.An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[J].arXiv preprint:1803.01271,2018:1-14.
[19]Fisher Yu,Vladlen Koltun.Multi-scale context aggregation by dilated convolutions[C]//International Conference on Learning Representations(ICLR).New Orleans,Puerto Rico:Universitéde Sherbrooke,2016:1-9.
[20]Tim S,Diederik P.Weight normalization:a simple parameterization to accelerate training of deep neural networks[C]//Neural Information Processing Systems(NIPS).Barcelona,Spain:University of Pennsylvania,2016:1-9.
[21]He Kaiming,Zhang Xiangyu,Ren Shaoqing,et al.Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Las Vegas,United States:IEEE,2016:770-778.
[22]韩花丽,张根保,杨微.湍流强度对风电机组测量功率曲线的影响及修正[J].太阳能学报,2015,36(6):1442-1447.Han Huali,Zhagn Genbao,Yang Wei,Influence and adjustment of turbulence intensity on measured power curve of wind turbine[J].Acta Energiae Solaris Sinica,2015,36(6):1442-1447(in Chinese).
[23]Sonia W,Julie K L.Atmospheric stability affects wind turbine power collection[J].Environmental Research Letters,2012,7(1):1-9.
[24]陈燕,马春燕,谭沛然,等.湍流值对风电功率预测的影响与分析[J].电测与仪表,2018,55(20):29-33.Chen Yan,Ma Chunyan,Tan Peiran,et al.Influence and analysis of turbulence value on wind power prediction[J].Electrical Measurement and Instrumentation,2018,55(20):29-33(in Chinese).
[25]Wang Xiaoming,Xie Yugang,Gao Bo,et al.Analysis of factors affecting wind farm output power[C]//2018 2nd IEEE Conference on Energy Internet and Energy System Integration(EI2).Beijing,China:IEEE,2018:1-5.
[26]徐曼,乔颖,鲁宗相.短期风电功率预测误差综合评价方法[J].电力系统自动化,2011,35(12):20-26.Xu Man,Qiao Ying,Lu Zongxiang.A comprehensive error evaluation method for short-term wind power prediction[J].Automation of Electric Power Systems.2011,35(12):20-26(in Chinese).