ULTRA-SHORT-TERM POWER FORECAST OF NEW ENERGY BASED ON METEOROLOGICAL FEATURE SELECTION AND SVM MODEL PARAMETER OPTIMIZATION

Chen Yuanfeng; Ma Xiyuan; Cheng Kai; Bao Tao; Chen Yansen; Zhou Changcheng

doi:10.19912/j.0254-0096.tynxb.2022-1401

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (12) : 568-576. DOI: 10.19912/j.0254-0096.tynxb.2022-1401

ULTRA-SHORT-TERM POWER FORECAST OF NEW ENERGY BASED ON METEOROLOGICAL FEATURE SELECTION AND SVM MODEL PARAMETER OPTIMIZATION

Chen Yuanfeng, Ma Xiyuan, Cheng Kai, Bao Tao, Chen Yansen, Zhou Changcheng

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Abstract

Under the major strategic deployment of building a new power system and realizing the "dual carbon" goal, new energy represented by Wind power generation and photovoltaic power generation will usher in a period of rapid development. New energy power generation is random, intermittent and volatile, and the integration of large-scale new energy power generation into the power grid will seriously affect the security, stability and economic operation of the power grid. Therefore, an ultra-short-term power prediction method for new energy power generation based on the selection of massive meteorological feature quantities and parameter optimization of support vector machine （SVM） model is proposed to improve the prediction accuracy of new energy power generation. Firstly, the meteorological feature extraction method based on the Pearson correlation coefficient is studied, and the parameters of the new energy power prediction support vector machine （SVM） model are optimized by using the particle swarm optimization （PSO）. The ultra short term prediction model of new energy power generation combined with meteorological feature extraction and model parameter optimization is further proposed to obtain the global optimal solution. The ultra short term rolling prediction model of new energy power generation is studied by using historical power generation data. Finally, the data of a domestic wind farm is used for comparison and verification, which proves that the proposed prediction model can effectively improve the prediction accuracy of new energy power generation.

Key words

new energy / forecasting / support vector machines / particle swarm optimization / feature extraction / parameter optimization

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Chen Yuanfeng, Ma Xiyuan, Cheng Kai, Bao Tao, Chen Yansen, Zhou Changcheng. ULTRA-SHORT-TERM POWER FORECAST OF NEW ENERGY BASED ON METEOROLOGICAL FEATURE SELECTION AND SVM MODEL PARAMETER OPTIMIZATION[J]. Acta Energiae Solaris Sinica. 2023, 44(12): 568-576 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1401

References

[1] 薛禹胜, 雷兴, 薛峰, 等. 关于风电不确定性对电力系统影响的评述[J]. 中国电机工程学报, 2014, 34(29): 5029-5040.
XUE Y S, LEI X, XUE F, et al.A review on impacts of wind power uncertainties on power systems[J]. Proceedings of the CSEE, 2014, 34(29): 5029-5040.
[2] 王俊杰, 毕利, 张凯, 等. 基于多特征融合和XGBoost-LightGBM-ConvLSTM的短期光伏发电量预测[J]. 太阳能学报, 2023, 44(7): 168-174.
WANG J J, BI L, ZHANG K, et al.Short-term photovoltaic power generation prediction based on multi-feature fusion and XGBoost-LightGBM-ConvLSTM[J]. Acta energiae solaris sinica, 2023, 44(7): 168-174.
[3] KARAKUŞ O, KURUOĞLU E E, ALTıNKAYA M A. One-day ahead wind speed/power prediction based on polynomial autoregressive model[J]. IET renewable power generation, 2017, 11(11): 1430-1439.
[4] 王飞, 甄钊, 刘兴杰. 新能源发电功率预测[M]. 北京: 科学出版社, 2020: 8-9.
WANG F, ZHEN Z, LIU X J.Renewable energy generation power forecasting[M]. Beijing: Science Press, 2020: 8-9.
[5] 朱琼锋, 李家腾, 乔骥, 等. 人工智能技术在新能源功率预测的应用及展望[J]. 中国电机工程学报, 2023, 43(8): 3027-3048.
ZHU Q F, LI J T, QIAO J, et al.Application and prospect of artificial intelligence technology in renewable energy forecasting[J]. Proceedings of the CSEE, 2023, 43(8): 3027-3048.
[6] YANG L, HE M, ZHANG J S, et al.Support-vector machine-enhanced Markov model for short term wind power forecast[J]. IEEE transactions on sustainable energy, 2015, 6(3): 791-799.
[7] 叶林, 路朋, 赵永宁, 等. 含风电电力系统有功功率模型预测控制方法综述[J]. 中国电机工程学报, 2021, 41(18): 6181-6198.
YE L, LU P, ZHAO Y N, et al.Review of model predictive control for power system with large-scale wind power grid-connected[J]. Proceedings of the CSEE, 2021, 41(18): 6181-6198.
[8] 赖昌伟, 黎静华, 陈博, 等. 光伏发电出力预测技术研究综述[J]. 电工技术学报, 2019, 34(6): 1201-1217.
LAI C W, LI J H, CHEN B, et al.Review of photovoltaic power output prediction technology[J]. Transactions of China Electrotechnical Society, 2019, 34(6): 1201-1217.
[9] 陈臣鹏, 赵鑫, 毕贵红, 等. 基于多模式分解和麻雀优化残差网络的短期风速预测模型[J]. 电网技术, 2022, 46(8): 2975-2985.
CHEN C P, ZHAO X, BI G H, et al.SSA-res-GRU short-term wind speed prediction model based on multi-model decomposition[J]. Power system technology, 2022, 46(8): 2975-2985.
[10] LI Y F, WANG Z Q, YU C Q. A new multi-data driven wind power forecasting model based on temporal reinforcement learning convolutional network(English)[J/OL]. Journal of Central South University, https://kns.cnki.net/kcms2/article/abstract?v=GARc9QQj0GXeEb-7jHGC9OyXQr_Y8wmwTdXUl2t7IafmN72T_ncRocf2LYJB_8g1ZiqOMu8-Q7MVB_xTCeOOqSjdZKxoz-lBdNmekSOKnIlvT4Pt7KvTgvtXoJ2d6TX_2Zn70WE0dtA=&uniplatform=NZKPT&language=CHS, 2021-05-27.
[11] 胡帅, 向月, 沈晓东, 等. 计及气象因素和风速空间相关性的风电功率预测模型[J]. 电力系统自动化, 2021, 45(7): 28-36.
HU S, XIANG Y, SHEN X D, et al.Wind power prediction model considering meteorological factor and spatial correlation of wind speed[J]. Automation of electric power systems, 2021, 45(7): 28-36.
[12] 田剑刚, 张沛, 彭春华, 等. 基于分时长短期记忆神经网络的光伏发电超短期功率预测[J]. 现代电力, 2020, 37(6): 629-638.
TIAN J G, ZHANG P, PENG C H, et al.Ultra short-term forecast of photovoltaic generation based on time-division long short-term memory neural networks[J]. Modern electric power, 2020, 37(6): 629-638.
[13] 张群, 唐振浩, 王恭, 等. 基于长短时记忆网络的超短期风功率预测模型[J]. 太阳能学报, 2021, 42(10): 275-281.
ZHANG Q, TANG Z H, WANG G, et al.Ultra-short-term wind power prediction model based on long and short term memory network[J]. Acta energiae solaris sinica, 2021, 42(10): 275-281.
[14] 刘云, 易松. 基于双参数最小二乘支持向量机(TPA-LSSVM)的风电时间序列预测模型的优化研究[J]. 北京化工大学学报(自然科学版), 2019, 46(2): 97-102.
LIU Y, YI S.Optimization of a wind power time series prediction model based on a two-parameter least squares support vector machine[J]. Journal of Beijing University of Chemical Technology (natural science edition), 2019, 46(2): 97-102.
[15] 张礽恺. 基于蜻蜓优化算法的风电超短期功率预测研究[D]. 石家庄: 河北科技大学, 2021.
ZHANG R K.Research on wind power ultra-short-term power prediction based on dragonfly optimization algorithm[D]. Shijiazhuang: Hebei University of Science and Technology, 2021.
[16] 李锦舒. 基于特征选择与数据挖掘的风电集群短期功率预测研究[D]. 武汉: 华中科技大学, 2020.
LI J S.Research on regional short term wind power prediction based on feature selection and data mining[D]. Wuhan: Huazhong University of Science and Technology, 2020.
[17] 丁志勇, 杨苹, 杨曦, 等. 基于连续时间段聚类的支持向量机风电功率预测方法[J]. 电力系统自动化, 2012, 36(14): 131-135, 149.
DING Z Y, YANG P, YANG X, et al.Wind power prediction method based on sequential time clustering support vector machine[J]. Automation of electric power systems, 2012, 36(14): 131-135, 149.
[18] 肖白, 聂鹏, 穆钢, 等. 基于多级聚类分析和支持向量机的空间负荷预测方法[J]. 电力系统自动化, 2015, 39(12): 56-61.
XIAO B, NIE P, MU G, et al.A spatial load forecasting method based on multilevel clustering analysis and support vector machine[J]. Automation of electric power systems, 2015, 39(12): 56-61.
[19] ZHAO Y N, YE L, PINSON P, et al.Correlation-constrained and sparsity-controlled vector autoregressive model for spatio-temporal wind power forecasting[J]. IEEE transactions on power systems, 2018, 33(5): 5029-5040.
[20] 吴杰康, 熊焰. 风水气互补发电优化的云模型自适应粒子群优化算法[J]. 中国电机工程学报, 2014, 34(S1): 17-24.
WU J K, XIONG Y.An cloud model theory based adaptive particle swarm optimization algorithm for complementary power generation optimization of wind-energy, hydro-energy and natural gas[J]. Proceedings of the CSEE, 2014, 34(S1): 17-24.
[21] 孙毅, 石墨, 单葆国, 等. 基于粒子群优化支持向量机的电能替代潜力分析方法[J]. 电网技术, 2017, 41(6): 1767-1771.
SUN Y, SHI M, SHAN B G, et al.Electric energy substitution potential analysis method based on particle swarm optimization support vector machine[J]. Power system technology, 2017, 41(6): 1767-1771.
[22] NB/T 10205—2019, 风电功率预测技术规定[S].
NB/T 10205—2019, Technical regulations for wind power prediction[S].
[23] CHEN N Y, QIAN Z, NABNEY I T, et al.Wind power forecasts using Gaussian processes and numerical weather prediction[J]. IEEE transactions on power systems, 2014, 29(2): 656-665.
[24] 杨宇晴, 张怡. 基于mRMR和VMD-AM-LSTM的短期风功率预测[J]. 控制工程, 2022, 29(1): 10-17.
YANG Y Q, ZHANG Y.Short-term wind power prediction based on mRMR and VMD-AM-LSTM[J]. Control engineering of China, 2022, 29(1): 10-17.
[25] 葛乐, 陆文伟, 袁晓冬, 等. 基于改进相似日和ABC-SVM的光伏电站功率预测[J]. 太阳能学报, 2018, 39(3): 775-782.
GE L, LU W W, YUAN X D, et al.Power forecasting of photovoltaic plant based on improved similar day and ABC-SVM[J]. Acta energiae solaris sinica, 2018, 39(3): 775-782.
[26] 倪超, 王聪, 朱婷婷, 等. 基于CNN-Bi-LSTM的太阳辐照度超短期预测[J]. 太阳能学报, 2022, 43(3): 197-202.
NI C, WANG C, ZHU T T, et al.Super-short-term forecast of solar irradiance based on CNN-Bi-LSTM[J]. Acta energiae solaris sinica, 2022, 43(3): 197-202.