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

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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

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