基于VMD-LSTM与误差补偿的光伏发电超短期功率预测

doi:10.19912/j.0254-0096.tynxb.2021-0043

太阳能学报 ›› 2022, Vol. 43 ›› Issue (8): 96-103.DOI: 10.19912/j.0254-0096.tynxb.2021-0043

基于VMD-LSTM与误差补偿的光伏发电超短期功率预测

王福忠, 王帅峰, 张丽

河南理工大学电气工程与自动化学院,焦作 454000

收稿日期:2021-01-11 出版日期:2022-08-28 发布日期:2023-02-28
通讯作者: 张丽（1982—）,女,博士、副教授,主要从事智能用电信息处理技术方面的研究。dqzhangli@hpu.edu.cn
基金资助:
国家自然科学基金（61403284）; 河南省科技攻关项目（202102210295; 212102210146）; 河南理工大学青年骨干项目（2019XQG-17）

ULTRA SHORT TERM POWER PREDICTION OF PHOTOVOLTAIC POWER GENERATION BASED ON VMD-LSTM AND ERROR COMPENSATION

Wang Fuzhong, Wang Shuaifeng, Zhang Li

School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China

Received:2021-01-11 Online:2022-08-28 Published:2023-02-28

摘要/Abstract

摘要： 光伏序列具有的较高复杂性对光伏发电功率的预测精度产生了极大影响,对此提出一种基于VMD-LSTM与误差补偿的光伏发电超短期功率预测模型。该模型第1阶段采用VMD算法将原始功率序列分解为若干个不同的模态,并对其建立对应的LSTM网络模型进行预测,通过对各模态的预测结果求和得到初始预测功率;第2阶段采用LSTM网络对误差序列进行误差补偿预测,然后将初始预测功率和误差预测功率求和得到最终预测结果。仿真结果表明,该预测模型对天气具有较高的适应性,预测精度达到97%以上。

关键词: 光伏发电, 功率预测, 深度学习, 长短期记忆, 变分模态分解, 误差补偿

Abstract: The high complexity of photovoltaic sequences has a great impact on the prediction accuracy of photovoltaic power generation. Therefore, an ultra-short-term power prediction model of photovoltaic power generation based on VMD-LSTM and error compensation is proposed. In the first stage of the model,the VMD algorithm is used to decompose the original power sequence into several different modes,and the corresponding LSTM network model is established for prediction,and the initial predicted power is obtained by summing the prediction results of each mode;In the second stage, the LSTM network is used to perform error compensation prediction on the error sequence,and then the initial prediction power and the error prediction power are summed to get the final prediction result. The simulation results show that the prediction model has high adaptability to the weather, and the prediction accuracy is over 97%.

Key words: PV power generation, power forecasting, deep learning, long short-term memory, variational mode decomposition, error compensation

中图分类号:

TM615

王福忠, 王帅峰, 张丽. 基于VMD-LSTM与误差补偿的光伏发电超短期功率预测[J]. 太阳能学报, 2022, 43(8): 96-103.

Wang Fuzhong, Wang Shuaifeng, Zhang Li. ULTRA SHORT TERM POWER PREDICTION OF PHOTOVOLTAIC POWER GENERATION BASED ON VMD-LSTM AND ERROR COMPENSATION[J]. Acta Energiae Solaris Sinica, 2022, 43(8): 96-103.

参考文献

[1] 丁明, 王伟胜, 王秀丽, 等. 大规模光伏发电对电力系统影响综述[J]. 中国电机工程学报, 2014, 34(1): 1-14.
DING M, WANG W S, WANG X L, et al.A review on the effect of large-scale PV generation on power systems[J]. Proceedings of the CSEE, 2014, 34(1): 1-14.
[2] GANDOMAN F H, ABDEL ALEEM S H E, OMAR N, et al. Short-term solar power forecasting considering cloud coverage and ambient temperature variation effects[J]. Renewable energy, 2018, 123(8): 793-805.
[3] KOSTER D, MINETTE F, BRAUN C, et al.Short-term and regionalized photovoltaic power forecasting, enhanced by reference systems, on the example of luxembourg[J]. Renewable energy, 2019, 132(3): 455-470.
[4] ESEYE A T, ZHANG J, ZHENG D.Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and meteorological information[J]. Renewable energy, 2017, 118(4): 357-367.
[5] FEI M, YI P, KEDONG Z, et al.A hybrid online forecasting model for ultrashort-term photovoltaic power generation[J]. Sustainability, 2018, 10(3): 820-836.
[6] HUANG C, CAO L, PENG N, et al.Day-ahead forecasting of hourly photovoltaic power based on robust multilayer perception[J]. Sustainability, 2018, 10(12): 4863-4870.
[7] 王育飞, 付玉超, 孙路, 等. 基于混沌-RBF神经网络的光伏发电功率超短期预测模型[J]. 电网技术, 2018, 42(4): 1110-1116.
WANG Y F, FU Y C, SUN L, et al.Ultra-short term prediction model of photovoltaic output power based on Chaos-RBF neural network[J]. Power system technology, 2018, 42(4): 1110-1116.
[8] 张晓波, 张保会, 吴雄. 风光预测后微电网的优化运行[J]. 电力自动化设备, 2016, 36(3): 21-25.
ZHANG X B, ZHANG B H, WU X.Ptimal microgrid operation based on wind/PV power prediction[J]. Electric power automation equipment, 2016, 36(3): 21-25.
[9] RODRIGUES S, MUTTER G, RAMOS H G.Machine learning photovoltaic string analyzer[J]. Entropy, 2020, 22(2): 205.
[10] 王小杨, 罗多, 孙韵琳, 等. 基于ABC-SVM和PSO-R的光伏微电网日发电功率组合预测方法研究[J]. 太阳能学报, 2020, 41(3): 177-183.
WANG X Y, LUO D, SUN Y L, et al.Combined forecasting method of daily photovoltaic power generation in microgrin based on ABC-SVM and PSO-RF models[J]. Acta energiae solaris sinica, 2020, 41(3): 177-183.
[11] SHENG H M, XIAO J, CHENG Y H, et al.Short-term solar power forecasting based on weighted gaussian process regression[J]. IEEE transactions on industrial electronics, 2018, 65(1): 300-308.
[12] 吉锌格, 李慧, 刘思嘉, 等. 基于MIE-LSTM的短期光伏功率预测[J]. 电力系统保护与控制, 2020, 48(7): 50-57.
JI X G, LI H, LIU S J, et al.Short-term photovoltaic power forecasting based on MIE-LSTM[J]. Power system protection and control, 2020, 48(7): 50-57.
[13] 高阳, 张碧玲, 毛京丽, 等. 基于机器学习的自适应光伏超短期出力预测模型[J]. 电网技术, 2015, 39(2): 307-311.
GAO Y, ZHANG B L, MAO J L, et al.Machine learning-based adaptive very-short-term forecast model for photovoltaic power[J]. Power system technology, 2015, 39(2): 307-311.
[14] LARSON D P, NONNENMACHER L, COIMBRA C F M. Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest[J]. Renewable energy, 2016, 91(7): 11-20.
[15] 阳霜, 罗滇生, 何洪英, 等. 基于EMD-LSSVM的光伏发电系统功率预测方法研究[J]. 太阳能学报, 2016, 37(6): 1387-1395.
YANG S, LUO D S, HE H Y, et al.Output power porecast of PV power system based on EMD-LSSVM model[J]. Acta energiae solaris sinica, 2016, 37(6): 1387-1395.
[16] LI P M, ZANG C Z, WANG K K, et al.Photovoltaic generation prediction based on similar days and neural network[J]. Renewable energy resources, 2013, 31(10): 1-4.
[17] 王粟, 江鑫, 曾亮, 等. 基于VMD-DESN-MSGP模型的超短期光伏功率预测[J]. 电网技术, 2020, 44(3): 917-926.
WANG L, JIANG X, ZENG L, et al.Ultra-short-term photovoltaic power prediction based on VMD-DESN-MSGP model[J]. Power system technology, 2020, 44(3): 917-926.

基于VMD-LSTM与误差补偿的光伏发电超短期功率预测

ULTRA SHORT TERM POWER PREDICTION OF PHOTOVOLTAIC POWER GENERATION BASED ON VMD-LSTM AND ERROR COMPENSATION

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