基于CEEMDAN-GSA-LSTM和SVR的光伏功率短期区间预测

doi:10.16183/j.cnki.jsjtu.2022.511

上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (6): 806-818.doi: 10.16183/j.cnki.jsjtu.2022.511

• 新型电力系统与综合能源 • 上一篇下一篇

基于CEEMDAN-GSA-LSTM和SVR的光伏功率短期区间预测

李芬¹(), 孙凌¹, 王亚维², 屈爱芳³, 梅念⁴, 赵晋斌¹

1.上海电力大学电气工程学院, 上海 200090
2.中国船舶集团有限公司第七二二研究所低频电磁通信技术实验室,武汉 430205
3.上海师范大学数理学院,上海 200234
4.国网经济技术研究院有限公司,北京 102209

收稿日期:2022-12-09 修回日期:2023-03-14 接受日期:2023-05-09 出版日期:2024-06-28 发布日期:2024-07-05
作者简介:李芬(1984-),博士,副教授,主要研究方向为新能源开发利用与电力变换技术; E-mail: lifen2012@shiep.edu.cn.
基金资助:
国家自然科学基金面上项目(52177184);上海绿色能源并网工程技术研究中心(13DZ2251900)

Short-Term Interval Forecasting of Photovoltaic Power Based on CEEMDAN-GSA-LSTM and SVR

LI Fen¹(), SUN Ling¹, WANG Yawei², QU Aifang³, MEI Nian⁴, ZHAO Jinbin¹

1. College of Electric Power Engineering, Shanghai University of Electric Power, Shanghai 200090, China
2. Laboratory of Low Frequency Electromagnetic Communication Technology, the 722 Research Institute, CSSC, Wuhan 430205, China
3. Mathematics and Science College, Shanghai Normal University, Shanghai 200234, China
4. State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China

Received:2022-12-09 Revised:2023-03-14 Accepted:2023-05-09 Online:2024-06-28 Published:2024-07-05

摘要/Abstract

摘要：

针对光伏输出功率存在间歇性和波动性的问题,提出一种光伏功率短期区间预测模型.首先,该模型采用自适应噪声完备集合经验模态分解将历史光伏出力数据分解为不同的分量并按照其与赤纬角、时角等时序特征量的相关性定义为时序分量和随机分量.其次,分别使用经过引力搜索算法优化的长短期记忆神经网络和支持向量回归模型对时序分量和随机分量进行预测.再次,叠加时序分量和随机分量的预测结果得到点预测结果.然后,对误差进行Johnson变换及正态分布建模后得到光伏功率区间预测结果.最后,利用算例验证该模型的有效性.结果表明:在不同天气情况下,上述模型比现有预测模型精度更高,具有较好的鲁棒性,能够基于预测值提供较为精准的置信区间.

关键词: 光伏功率预测, 区间预测, 自适应噪声完备集合经验模态分解, 引力搜索算法, 长短期记忆, 支持向量回归, Johnson变换

Abstract:

Aimed at the intermittency and fluctuation of photovoltaic output power, a short-term interval prediction model of photovoltaic power is proposed. First, the model uses the complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN) to decompose the historical photovoltaic output data into different components and define them as time-series components and random components according to their correlation with time-series features such as declination and time angles. Then, the long short-term memory (LSTM) neural network and the support vector regression (SVR) model optimized by the gravitational search algorithm (GSA) are used to predict the time series components and the random components respectively, and the prediction results of the time series components and the random components are superimposed to obtain the point prediction result. After the error is subjected to Johnson transformation and normal distribution modeling, the photovoltaic power interval prediction result is obtained. Finally, the effectiveness of the method is verified by an example. The comparison of the proposed model with other existing prediction models under different weather conditions suggests that the proposed model has a higher accuracy and a better robustness, which can provide precise confidence intervals based on point prediction values.

Key words: photovoltaic power prediction, interval prediction, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gravitational search algorithm (GSA), long short-term memory (LSTM), support vector regression (SVR), Johnson transformation

中图分类号:

TM615
P49

李芬, 孙凌, 王亚维, 屈爱芳, 梅念, 赵晋斌. 基于CEEMDAN-GSA-LSTM和SVR的光伏功率短期区间预测[J]. 上海交通大学学报, 2024, 58(6): 806-818.

LI Fen, SUN Ling, WANG Yawei, QU Aifang, MEI Nian, ZHAO Jinbin. Short-Term Interval Forecasting of Photovoltaic Power Based on CEEMDAN-GSA-LSTM and SVR[J]. Journal of Shanghai Jiao Tong University, 2024, 58(6): 806-818.

图/表 17

图1

图2

表1

Johnson变换的3种类型

分布系列	g(·)	约束条件
S_B	ln $X - ξ ξ + λ - X$	ξ<X<ξ+λ,λ>0
S_L	ln $X - ξ λ$	X>ξ,λ>0
S_U	arsinh $X - ξ λ$	λ>0

表1

图3

图4

图5

表2

表3

表4

图6

图7

表5

图8

图9

图10

表6

表7

参考文献 26

[1]	国家发展改革会, 国家能源局. 关于促进新时代新能源高质量发展的实施方案[EB/OL].(2022-05-30)[2022-05-30]. http://zfxxgk.nea.gov.cn/2022-05/30/c_1310608539.htm.
	National Development and Reform Commission, National Energy Administration. The implementation plan for promoting the high-quality development of new energy in a new era[EB/OL].(2022-05-30)[2022-05-30]. http://zfxxgk.nea.gov.cn/2022-05/30/c_1310608539.htm.
[2]	张雲钦, 程起泽, 蒋文杰, 等. 基于EMD-PCA-LSTM的光伏功率预测模型[J]. 太阳能学报, 2021, 42(9): 62-69.
	ZHANG Yunqin, CHENG Qize, JIANG Wenjie, et al. Photovoltaic power prediction model based on EMD-PCA-LSTM[J]. Acta Energiae Solaris Sinica, 2021, 42(9): 62-69.
[3]	叶林, 马明顺, 靳晶新, 等. 考虑风电-光伏功率相关性的因子分析-极限学习机聚合方法[J]. 电力系统自动化, 2021, 45(23): 31-40.
	YE Lin, MA Mingshun, JIN Jingxin, et al. Factor analysis-extreme learning machine aggregation method considering correlation of wind power and photovoltaic power[J]. Automation of Electric Power Systems, 2021, 45(23): 31-40.
[4]	黎嘉明, 艾小猛, 文劲宇, 等. 光伏发电功率持续时间特性的概率分布定量分析[J]. 电力系统自动化, 2017, 41(6): 30-36.
	LI Jiaming, AI Xiaomeng, WEN Jinyu, et al. Quantitative analysis of probability distribution for duration time characteristic of photovoltaic power[J]. Automation of Electric Power Systems, 2017, 41(6): 30-36.
[5]	李芬, 周尔畅, 孙改平, 等. 一种新型天气分型方法及其在光伏功率预测中的应用[J]. 上海交通大学学报, 2021, 55(12): 1510-1519. doi: 10.16183/j.cnki.jsjtu.2021.264
	LI Fen, ZHOU Erchang, SUN Gaiping, et al. A novel weather classification method and its application in photovoltaic power prediction[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1510-1519.
[6]	唐雅洁, 林达, 倪筹帷, 等. 基于XGBoost的双层协同实时校正超短期光伏预测[J]. 电力系统自动化, 2021, 45(7): 18-27.
	TANG Yajie, LIN Da, NI Chouwei, et al. XGBoost based Bi-layer collaborative real-time calibration for ultra-short-term photovoltaic prediction[J]. Automation of Electric Power Systems, 2021, 45(7): 18-27.
[7]	AGGA A, ABBOU A, LABBADI M, et al. CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production[J]. Electric Power Systems Research, 2022, 208: 107908.
[8]	万灿, 崔文康, 宋永华. 新能源电力系统概率预测:基本概念与数学原理[J]. 中国电机工程学报, 2021, 41(19): 6493-6509.
	WAN Can, CUI Wenkang, SONG Yonghua. Probabilistic forecasting for power systems with renewable energy sources: Basic concepts and mathematical principles[J]. Proceedings of the CSEE, 2021, 41(19): 6493-6509.
[9]	黎敏, 林湘宁, 张哲原, 等. 超短期光伏出力区间预测算法及其应用[J]. 电力系统自动化, 2019, 43(3): 10-16.
	LI Min, LIN Xiangning, ZHANG Zheyuan, et al. Interval prediction algorithm for ultra-short-term photovoltaic output and its Application[J]. Automation of Electric Power Systems, 2019, 43(3): 10-16.
[10]	杨茂, 王凯旋. 基于CEEMD-DBN模型的光伏出力日前区间预测[J]. 高电压技术, 2021, 47(4): 1156-1164.
	YANG Mao, WANG Kaixuan. Day-ahead interval forecasting of PV power based on CEEMD-DBN model[J]. High Voltage Engineering, 2021, 47(4): 1156-1164.
[11]	茆美琴, 龚文剑, 张榴晨, 等. 基于EEMD-SVM方法的光伏电站短期出力预测[J]. 中国电机工程学报, 2013, 33(34): 17-24.
	MAO Meiqin, GONG Wenjian, ZHANG Liuchen, et al. Short-term photovoltaic generation forecasting based on EEMD-SVM combined method[J]. Proceedings of the CSEE, 2013, 33(34): 17-24.
[12]	ZHANG C, HUA L, JI C L, et al. An evolutionary robust solar radiation prediction model based on WT-CEEMDAN and IASO-optimized outlier robust extreme learning machine[J]. Applied Energy, 2022, 322: 119518.
[13]	RASHEDI E, NEZAMABADI-POUR H, SARYAZDI S. GSA: A gravitational search algorithm[J]. Information Sciences, 2009, 179(13): 2232-2248.
[14]	ALI KHAN T, LING S H. A novel hybrid gravitational search particle swarm optimization algorithm[J]. Engineering Applications of Artificial Intelligence, 2021, 102: 104263.
[15]	杨晶显, 张帅, 刘继春, 等. 基于VMD和双重注意力机制LSTM的短期光伏功率预测[J]. 电力系统自动化, 2021, 45(3): 174-182.
	YANG Jingxian, ZHANG Shuai, LIU Jichun, et al. Short-term photovoltaic power prediction based on variational mode decomposition and long shortterm memory with dual-stage attention mechanism[J]. Automation of Electric Power Systems, 2021, 45(3): 174-182.
[16]	苏向敬, 周汶鑫, 李超杰, 等. 基于双重注意力LSTM神经网络的可解释海上风电出力预测[J]. 电力系统自动化, 2022, 46(7): 141-151.
	SU Xiangjing, ZHOU Wenxin, LI Chaojie, et al. Interpretable offshore wind power output forecasting based on long short-term memory neural network with dual-stage attention[J]. Automation of Electric Power Systems, 2022, 46(7): 141-151.
[17]	LIU R H, WEI J C, SUN G P, et al. A short-term probabilistic photovoltaic power prediction method based on feature selection and improved LSTM neural network[J]. Electric Power Systems Research, 2022, 210: 108069.
[18]	LI J L, SONG Z H, WANG X F, et al. A novel offshore wind farm typhoon wind speed prediction model based on PSO-Bi-LSTM improved by VMD[J]. Energy, 2022, 251: 123848.
[19]	董春曦, 饶鲜, 杨绍全, 等. 支持向量机参数选择方法研究[J]. 系统工程与电子技术, 2004, 26(8): 1117-1120.
	DONG Chunxi, RAO Xian, YANG Shaoquan, et al. Method for selecting the parameters of support vector machines[J]. Systems Engineering & Electronics, 2004, 26(8): 1117-1120.
[20]	徐浩, 姜新雄, 刘志成, 等. 基于概率预测的电网静态安全运行风险评估及主动调控策略[J]. 电力系统自动化, 2022, 46(1): 182-191.
	XU Hao, JIANG Xinxiong, LIU Zhicheng, et al. Probability prediction based risk assessment and proactive regulation and control strategy for static operation safety of power grid[J]. Automation of Electric Power Systems, 2022, 46(1): 182-191.
[21]	WANG H L, XU D X, MARTINEZ A. Parameter selection method for support vector machine based on adaptive fusion of multiple kernel functions and its application in fault diagnosis[J]. Neural Computing & Applications, 2020, 32(1): 183-193.
[22]	SLIFKER J F, SHAPIRO S S. The Johnson system: Selection and parameter estimation[J]. Technometrics, 1980, 22(2): 239-246.
[23]	HE Y Y, ZHENG Y Y. Short-term power load probability density forecasting based on Yeo-Johnson transformation quantile regression and Gaussian kernel function[J]. Energy, 2018, 154: 143-156.
[24]	汪冬华. 多元统计分析与SPSS应用[M]. 上海: 华东理工大学出版社, 2010.
	WANG Donghua. Multivariate statistical analysis and SPSS application[M]. Shanghai: East China University of Science and Technology Press, 2010.
[25]	LI F, LIN Y L, GUO J P, et al. Novel models to estimate hourly diffuse radiation fraction for global radiation based on weather type classification[J]. Renewable Energy, 2020, 157: 1222-1232.
[26]	DEMAIN C, JOURNÉE M, BERTRAND C. Evaluation of different models to estimate the global solar radiation on inclined surfaces[J]. Renewable Energy, 2013, 50: 710-721.

天气	分量	GSA-LSTM		LSTM		BP		SVR
天气	分量	e_S/%	e_N/%	e_S/%	e_N/%	e_S/%	e_N/%	e_S/%	e_N/%
晴天	H_IMF1	20.3	3.7	27.5	4.8	54.4	15.0	50.5	13.2
晴天	H_IMF2	21.4	3.5	26.3	5.2	52.3	14.3	49.4	14.5
晴天	H_IMF3	18.7	2.6	21.9	3.7	47.2	12.1	48.8	13.6
晴天	H_IMF7	18.2	2.1	19.9	3.6	48.1	9.9	41.0	10.3
转折	H_IMF1	47.0	10.1	59.5	14.7	94.4	44.2	89.2	42.7
转折	H_IMF2	39.2	9.7	60.3	15.6	94.6	43.1	87.6	42.0
转折	H_IMF3	35.4	6.4	52.0	12.3	88.7	38.5	88.7	36.3
转折	H_IMF7	29.1	5.5	52.3	13.3	83.3	40.3	82.1	36.9
阴雨	H_IMF1	45.5	13.2	70.6	44.0	110.5	55.5	100.3	57.2
阴雨	H_IMF2	40.3	12.1	65.5	42.5	103.2	52.4	97.0	51.4
阴雨	H_IMF3	41.2	10.1	71.3	35.4	99.9	43.6	92.3	40.2
阴雨	H_IMF7	38.9	7.8	62.2	32.1	101.2	44.2	90.1	41.1

天气	分量	GSA-SVR		LSTM		BP		SVR
天气	分量	e_S/%	e_N/%	e_S/%	e_N/%	e_S/%	e_N/%	e_S/%	e_N/%
晴天	H_IMF4	25.7	7.1	39.0	10.5	42.3	10.0	36.2	9.2
晴天	H_IMF5	24.3	6.8	38.7	10.7	37.0	9.6	27.9	9.8
晴天	H_IMF6	22.4	5.6	32.2	8.8	31.1	8.4	30.2	8.0
转折	H_IMF4	60.0	17.5	75.4	34.7	86.3	35.4	67.8	22.3
转折	H_IMF5	57.1	20.3	69.7	32.8	83.3	34.9	62.4	24.6
转折	H_IMF6	50.9	15.4	75.2	30.9	75.1	31.6	55.3	18.7
阴雨	H_IMF4	62.4	20.5	87.3	42.4	99.9	45.4	69.4	22.9
阴雨	H_IMF5	56.2	19.6	89.0	40.0	97.1	41.0	64.4	27.1
阴雨	H_IMF6	54.8	18.7	83.5	34.9	89.0	35.7	59.9	21.0

天气	CEEMDAN-GSA-LSTM 和SVR		EMD-GSA-LSTM		EMD-GSA-SVR		XGBoost-RC		CNN-LSTM
天气	e_S/%	e_N/%	e_S/%	e_N/%	e_S/%	e_N/%	e_S/%	e_N/%	e_S/%	e_N/%
晴天	22.8	3.7	29.6	5.0	38.6	9.1	30.1	4.5	24.2	9.1
转折	40.0	9.1	61.8	16.0	64.1	22.2	47.1	21.2	55.4	10.1
阴雨	48.5	14.6	78.4	40.3	63.0	26.2	54.7	32.5	50.7	19.6

数据组别	CEEMDAN-GSA-LSTM和SVR		EMD-GSA-LSTM		EMD-GSA-SVR
数据组别	e_S/%	e_N/%	e_S/%	e_N/%	e_S/%	e_N/%
原始历史数据	48.5	14.6	78.4	40.3	63.0	26.2
D1	72.3	36.5	94.4	48.4	85.8	39.2
D2	73.4	38.9	98.9	53.3	91.1	57.1
D3	203.5	250.4	200.4	287.5	241.1	329.9
D4	212.3	233.9	240.1	298.8	242.9	310.5

置信水平/ %	晴天		转折		阴雨
置信水平/ %	Q_PICP	Q_PINAW	Q_PICP	Q_PINAW	Q_PICP	Q_PINAW
95	1.0000	0.1307	0.9697	0.2393	0.9394	0.2238
80	0.9394	0.0984	0.9394	0.1876	0.9091	0.1733
60	0.8485	0.0703	0.8182	0.1382	0.6970	0.1282
40	0.6364	0.0460	0.6364	0.0923	0.4848	0.0864

基于CEEMDAN-GSA-LSTM和SVR的光伏功率短期区间预测

Short-Term Interval Forecasting of Photovoltaic Power Based on CEEMDAN-GSA-LSTM and SVR

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 17

参考文献 26

相关文章 15

编辑推荐

Metrics

本文评价

[1]	何之倬, 张颖, 郑刚, 郑芳, 黄琬迪, 张沈习, 程浩忠. 基于极限学习机模型参数优化的光伏功率区间预测技术[J]. 上海交通大学学报, 2024, 58(3): 285-294.
[2]	朱昶胜，朱丽娜. 基于经验小波变换、循环神经网络和误差校正的短期风速预测[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(2): 297-308.
[3]	卫慧, 陈鹏, 张芮菡, 程正顺. 基于长短期记忆网络的大型漂浮式风力发电机平台运动极短期预报方法[J]. 上海交通大学学报, 2023, 57(S1): 37-45.
[4]	尚凡成, 李传庆, 詹可, 朱仁传. 改进LSTM神经网络在极短期波浪时序预报中的应用[J]. 上海交通大学学报, 2023, 57(6): 659-665.
[5]	张博, 李克庆, 胡亚飞, 吉坤, 韩斌. 基于灰狼优化算法改进支持向量回归的充填体强度预测研究[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(5): 686-694.
[6]	曹现刚1, 2，雷卓1，李彦川1，张梦园1，段欣宇1. 基于Self-Attention-LSTM神经网络的设备剩余寿命预测方法[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(5): 652-664.
[7]	万安平, 杨洁, 缪徐, 陈挺, 左强, 李客. 基于注意力机制与神经网络的热电联产锅炉负荷预测[J]. 上海交通大学学报, 2023, 57(3): 316-325.
[8]	李琰, 肖龙飞, 魏汉迪, 寇雨丰. 基于长短期记忆网络的半潜平台波浪爬升预报[J]. 上海交通大学学报, 2023, 57(2): 161-167.
[9]	刘钇汛, 刘志浩, 高钦和, 黄通, 马栋. 基于周向应变分析的重载轮胎垂向力估计算法[J]. 上海交通大学学报, 2023, 57(10): 1273-1281.
[10]	. 行人轨迹预测的动作感知编码器–解码器网络[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(1): 20-27.
[11]	朱城昊, 王晗, 孙国歧, 魏晓宾, 王富文, 蔡旭. 一种并网逆变器直流电容容值辨识方法[J]. 上海交通大学学报, 2022, 56(6): 693-700.
[12]	徐宏东, 高海波, 徐晓滨, 林治国, 盛晨兴. 基于证据推理规则CS-SVR模型的锂离子电池SOH估算[J]. 上海交通大学学报, 2022, 56(4): 413-421.
[13]	卢地华, 陈自强. 基于双充电状态的锂离子电池健康状态估计[J]. 上海交通大学学报, 2022, 56(3): 342-352.
[14]	姜淏予, 王沛伦, 葛泉波, 徐今强, 罗朋, 姚刚. 漂浮式光伏网格对海上天气突变的感知方法[J]. 上海交通大学学报, 2022, 56(12): 1584-1597.
[15]	武光利, 郭振洲, 李雷霆, 王成祥. 融合FCN和LSTM的视频异常事件检测[J]. 上海交通大学学报, 2021, 55(5): 607-614.