基于相似日选取和PCA-LSTM的光伏出力组合预测模型研究

孟亦康; 许野; 王鑫鹏; 王涛; 李薇

doi:10.19912/j.0254-0096.tynxb.2023-0498

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (7) : 453-461. DOI: 10.19912/j.0254-0096.tynxb.2023-0498

基于相似日选取和PCA-LSTM的光伏出力组合预测模型研究

孟亦康, 许野, 王鑫鹏, 王涛, 李薇

作者信息 +

RESEARCH ON PHOTOVOLTAIC OUTPUT COMBINATION PREDICTION MODEL BASED ON SIMILAR DAY SELECTION AND PCA-LSTM

Meng Yikang, Xu Ye, Wang Xinpeng, Wang Tao, Li Wei

Author information +

文章历史 +

摘要

构建一套融合主成分分析方法（PCA）、改进的K-均值聚类方法、动态时间规整算法（DTW）和长短期记忆神经网络（LSTM）的光伏出力组合预测模型。在运用PCA法提取气象要素的主成分因子的基础上,创新性地联合使用改进的K-均值聚类方法和DTW算法生成内部关联程度高且与待预测日的天气特征相近的历史日样本集;然后,结合LSTM神经网络,构建基于相似日选取的光伏发电功率预测模型,最终实现了云南某光伏电站发电功率的精准预测。与其他预测模型的对比结果显示,该文构建的组合预测模型具备更好的预测性能和广阔的应用前景。

Abstract

In this paper, a PV output portfolio forecasting model is constructed by integrating principal component analysis （PCA）, an improved K-means clustering method, dynamic time warping （DTW）, and a long-short term memory （LSTM） neural network. Based on the PCA method to extract the principal component factors of meteorological elements, the improved K-means clustering method and DTW algorithm are innovatively used to generate a set of historical day samples with a high degree of internal correlation and similar weather characteristics to the day to be predicted. Then, the LSTM neural network is combined to build a PV power prediction model based on the selection of similar days, which finally achieves the accurate prediction of power generation of a PV plant in Yunnan. The comparison results with other prediction models show that the combined prediction model constructed in this paper has better prediction performance and broad application prospects.

导出引用

孟亦康, 许野, 王鑫鹏, 王涛, 李薇. 基于相似日选取和PCA-LSTM的光伏出力组合预测模型研究[J]. 太阳能学报. 2024, 45(7): 453-461 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0498

Meng Yikang, Xu Ye, Wang Xinpeng, Wang Tao, Li Wei. RESEARCH ON PHOTOVOLTAIC OUTPUT COMBINATION PREDICTION MODEL BASED ON SIMILAR DAY SELECTION AND PCA-LSTM[J]. Acta Energiae Solaris Sinica. 2024, 45(7): 453-461 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0498

中图分类号： TM615

参考文献

[1] 张志文, 杜泽源, 罗熹. 大规模光伏发电对电力系统影响[J]. 科技视界, 2018(3): 138-139.
ZHANG Z W, DU Z Y, LUO X.Impact of large scale photovoltaic power generation on power system[J]. Science ＆ technology vision, 2018(3): 138-139.
[2] 吴硕. 光伏发电系统功率预测方法研究综述[J]. 热能动力工程, 2021, 36(8): 1-7.
WU S.Review of power forecasting methods of photovoltaic power generation system[J]. Journal of engineering for thermal energy and power, 2021, 36(8): 1-7.
[3] 李烁, 陈新度, 尹玲, 等. 考虑气象变化的光伏发电模型评估及研究[J]. 太阳能学报, 2022, 43(6): 79-84.
LI S, CHEN X D, YIN L, et al.Evaluation and research of photovoltaic power generation model considering climate change[J]. Acta energiae solaris sinica, 2022, 43(6): 79-84.
[4] 李昊琦, 魏利平, 庄子贤, 等. 光伏相变系统温控特性及散热结构优化设计[J]. 太阳能学报, 2022, 43(9): 57-63.
LI H Q, WEI L P, ZHUANG Z X, et al.Temperature control characteristics and heat dissipation structure optimization design of photovoltaic phase change system[J]. Acta energiae solaris sinica, 2022, 43(9): 57-63.
[5] 谭海旺, 杨启亮, 邢建春, 等. 基于XGBoost-LSTM组合模型的光伏发电功率预测[J]. 太阳能学报, 2022, 43(8): 75-81.
TAN H W, YANG Q L, XING J C, et al.Photovoltaic power prediction based on combined XGBoost-LSTM model[J]. Acta energiae solaris sinica, 2022, 43(8): 75-81.
[6] 乔路丽, 方诗琦, 赵庭锐, 等. 基于相似日和IGA-BP的光伏发电功率预测方法研究[J]. 电网与清洁能源, 2022, 38(1): 128-134.
QIAO L L, FANG S Q, ZHAO T R, et al.A study on the forecasting method of photovoltaic power generation based on similar day and IGA-BP[J]. Power system and clean energy, 2022, 38(1): 128-134.
[7] 魏联滨, 王彬, 王莹, 等. 基于气象相似日选取与提升回归树的光伏发电短期功率预测[J]. 电子器件, 2022, 45(1): 183-188.
WEI L B, WANG B, WANG Y, et al.Short-term power forecast of photo-voltaic power generation based on weather similarity day and boosting regression tree[J]. Chinese journal of electron devices, 2022, 45(1): 183-188.
[8] ZHOU Y, ZHOU N R, GONG L H, et al.Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine[J]. Energy, 2020, 204: 117894.
[9] LIU X J, LIU Y Y, KONG X B, et al.Deep neural network for forecasting of photovoltaic power based on wavelet packet decomposition with similar day analysis[J]. Energy, 2023, 271: 126963.
[10] 程启明, 张强, 程尹曼, 等. 基于密度峰值层次聚类的短期光伏功率预测模型[J]. 高电压技术, 2017, 43(4): 1214-1222.
CHENG Q M, ZHANG Q, CHENG Y M, et al.Short-term photovoltaic power prediction model based on hierarchical clustering of density peaks algorithm[J]. High voltage engineering, 2017, 43(4): 1214-1222.
[11] 李刚, 刘佳林, 王腾飞, 等. 基于相似日理论和IPSO-Elman模型的短期光伏发电功率预测[J]. 测控技术, 2020, 39(2): 91-97, 131.
LI G, LIU J L, WANG T F, et al.Short-term photovoltaic power forecast based on similar day theory and IPSO-Elman model[J]. Measurement & control technology, 2020, 39(2): 91-97, 131.
[12] 刘新志. 环境因素影响下的相似日分析与短期负荷预测[D]. 昆明: 昆明理工大学, 2021.
LIU X Z.Similar day analysis and short-term load forecasting under the influence of environmental factors[D]. Kunming:Kunming University of Science and Technology, 2021.
[13] 张雨金, 杨凌帆, 葛双冶, 等. 基于K-均值-SVM的短期光伏发电功率预测[J]. 电力系统保护与控制, 2018, 46(21): 118-124.
ZHANG Y J, YANG L F, GE S Y, et al.Short-term photovoltaic power forecasting based on K-means algorithm and support vector machine[J]. Power system protection and control, 2018, 46(21): 118-124.
[14] 郭超凡, 王旭明, 石晨宇, 等. 基于改进K均值算法的玉米叶片图像分割[J]. 中北大学学报(自然科学版), 2021, 42(6): 524-529.
GUO C F, WANG X M, SHI C Y, et al.Corn leaf image segmentation based on improved K-means algorithm[J]. Journal of North University of China (natural science edition), 2021, 42(6): 524-529.
[15] 凌玉龙, 张晓, 李霞, 等. 改进K-均值算法在学生消费画像中的应用[J]. 计算机技术与发展, 2021, 31(10): 122-127.
LING Y L, ZHANG X, LI X, et al.Application of improved K-means algorithm in student consumption portrait[J]. Computer technology and development, 2021, 31(10): 122-127.
[16] 王鹏翔, 沈娟, 王菁旸, 等. 基于PCA-LMD-WOA-ELM的短期光伏功率预测[J]. 智慧电力, 2022, 50(6): 72-78.
WANG P X, SHEN J, WANG J Y, et al.Short term photovoltaic power prediction based on PCA-LMD-WOA-ELM[J]. Smart power, 2022, 50(6): 72-78.
[17] 李秉晨, 于惠钧, 刘靖宇. 基于K-均值和CEEMD-PE-LSTM的短期光伏发电功率预测[J]. 水电能源科学, 2021, 39(4): 204-208.
LI B C, YU H J, LIU J Y.Prediction of short-term photovoltaic power generation based on K-means and CEEMD-PE-LSTM[J]. Water resources and power, 2021, 39(4): 204-208.
[18] 张海燕, 闫文君, 张立民, 等. 基于微分思想和动态时间弯曲(DTW)的飞行员着舰技能评估研究[J]. 兵器装备工程学报, 2023, 44(3): 124-130.
ZHANG H Y, YAN W J, ZHANG L M, et al.Evaluation of pilot landing skills based on differential thinking and dynamic time warping[J]. Journal of ordnance equipment engineering, 2023, 44(3): 124-130.
[19] CHEN H L, CHANG X F.Photovoltaic power prediction of LSTM model based on Pearson feature selection[J]. Energy reports, 2021, 7: 1047-1054.
[20] MA W T, LEI Y M, WANG X F, et al.Robust state of charge estimation of lithium-ion battery via mixture kernel mean p-power error loss LSTM with heap-based-optimizer[J]. Journal of energy chemistry, 2023, 80: 768-784.