基于极限学习机模型参数优化的光伏功率区间预测技术

doi:10.16183/j.cnki.jsjtu.2022.338

上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (3): 285-294.doi: 10.16183/j.cnki.jsjtu.2022.338

• 新型电力系统与综合能源 • 上一篇下一篇

基于极限学习机模型参数优化的光伏功率区间预测技术

何之倬¹, 张颖¹(), 郑刚¹, 郑芳¹, 黄琬迪², 张沈习², 程浩忠²

1.国网上海市电力公司青浦供电公司,上海 201700
2.上海交通大学电力传输与功率变换控制教育部重点实验室,上海 200240

收稿日期:2022-08-30 修回日期:2022-11-20 接受日期:2022-12-08 出版日期:2024-03-28 发布日期:2024-03-28
通讯作者: 张颖,博士,高级工程师;E-mail: zhyingt@163.com.
作者简介:何之倬(1990-),硕士,工程师,从事分布式光伏并网相关研究.
基金资助:
国网上海市电力公司科技项目(52093421N001);国家自然科学基金(51907123)

Interval Prediction Technology of Photovoltaic Power Based on Parameter Optimization of Extreme Learning Machine

HE Zhizhuo¹, ZHANG Ying¹(), ZHENG Gang¹, ZHENG Fang¹, HUANG Wandi², ZHANG Shenxi², CHENG Haozhong²

1. State Grid Shanghai Qingpu Electric Power Supply Company, Shanghai 201700, China
2. Key Laboratory of Control of Power Transmission and Conversion of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2022-08-30 Revised:2022-11-20 Accepted:2022-12-08 Online:2024-03-28 Published:2024-03-28

摘要/Abstract

摘要：

提出一种基于极限学习机(ELM)模型参数优化的光伏功率区间预测技术.首先,提出加权欧氏距离作为光伏功率预测区间评估指标,筛选历史样本单元并优化ELM训练集;然后,提出ELM参数混合寻优算法,利用精英保留策略遗传算法与分位数回归优化ELM模型隐层输入及输出权重与偏置参数,并采用训练后的模型预测光伏功率区间;最后,基于光伏电站与气象站历史数据构建实际算例,预测光伏功率区间,并与其他方法得到的结果进行对比.算例结果表明:所提方法在增加区间预测可信度的同时,能较大程度提高区间预测准确度.

关键词: 光伏功率, 区间预测, 极限学习机, 参数优化, 加权欧氏距离指标

Abstract:

This paper proposes an interval prediction technology of photovoltaic (PV) power based on parameter optimization of extreme learning machine (ELM) model. First, the weighted Euclidean distance is proposed as the evaluation index of PV power prediction interval. The historical sample units are screened and the ELM training set is optimized. Then, a hybrid optimization algorithm for ELM parameters is proposed. The hidden layer input and output weights and biases parameters of the ELM model are optimized by using the elitist strategy genetic algorithm and quantile regression, and the trained model is used to predict the PV power range. Finally, an actual calculation example is constructed based on the historical data of PV power plants and weather stations. The PV power interval is predicted, and the results are compared with those obtained by other methods. The results of the calculation example show that the method proposed can greatly improve the accuracy of interval prediction while increasing the reliability of interval prediction.

Key words: photovoltaic (PV) power, interval prediction, extreme learning machine (ELM), parameter optimization, weighted Euclidean distance index

中图分类号:

TM615

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.

HE Zhizhuo, ZHANG Ying, ZHENG Gang, ZHENG Fang, HUANG Wandi, ZHANG Shenxi, CHENG Haozhong. Interval Prediction Technology of Photovoltaic Power Based on Parameter Optimization of Extreme Learning Machine[J]. Journal of Shanghai Jiao Tong University, 2024, 58(3): 285-294.

图/表 10

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参考文献 28

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p_PINC/%	p_PICP/%	p_PINAW/%
95	95.12	2.69
90	95.12	1.93
85	92.68	1.48
80	87.80	1.18

p_PINC /%	p_PICP/%	p_PINAW/%
95	100	12.87
90	100	6.23
85	97.56	3.67
80	90.24	3.03

p_PINC /%	p_PICP/%	p_PINAW/%
95	97.73	7.87
90	97.73	4.85
85	95.45	3.36
80	90.91	2.67

日期	p_PINC/%	方法I		方法II		方法III		方法IV
日期	p_PINC/%	p_PICP/%	p_PINAW/%	p_PICP/%	p_PINAW/%	p_PICP/%	p_PINAW/%	p_PICP/%	p_PINAW/%
7月1日	95	95.12	2.69	95.12	2.78	97.56	7.82	93.90	3.00
7月1日	90	95.12	1.93	92.68	1.95	97.56	4.50	88.78	2.01
7月1日	85	92.68	1.48	92.68	1.60	95.12	4.42	86.83	1.51
7月1日	80	87.80	1.18	87.80	1.21	90.24	3.91	85.37	1.23
8月3日	95	100.00	12.87	100.00	14.31	100.00	14.35	99.76	14.27
8月3日	90	100.00	6.23	100.00	6.95	97.56	7.11	99.27	6.93
8月3日	85	97.56	3.67	95.12	3.71	95.12	4.66	97.07	3.81
8月3日	80	90.24	3.03	90.24	3.11	95.12	3.99	92.44	3.12
9月10日	95	97.73	7.87	100.00	8.52	100.00	22.30	97.50	9.28
9月10日	90	97.73	4.85	100.00	4.97	100.00	8.92	96.14	5.47
9月10日	85	95.45	3.36	93.18	3.64	95.45	8.46	95.68	3.64
9月10日	80	90.91	2.67	88.64	2.77	95.45	5.37	92.56	2.91

基于极限学习机模型参数优化的光伏功率区间预测技术

Interval Prediction Technology of Photovoltaic Power Based on Parameter Optimization of Extreme Learning Machine

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