基于相似日选取和数据重构的短期光伏功率组合预测方法

doi:10.11930/j.issn.1004-9649.202406005

中国电力 ›› 2024, Vol. 57 ›› Issue (12): 71-81.DOI: 10.11930/j.issn.1004-9649.202406005

• 面向新型电力系统的源荷预测技术 • 上一篇下一篇

基于相似日选取和数据重构的短期光伏功率组合预测方法

陈庆斌¹(), 杨耿煌¹^,²(), 耿丽清¹^,², 苏娟³, 孙京生⁴

1. 天津职业技术师范大学自动化与电气工程学院，天津　300222
2. 天津市信息传感与智能控制重点实验室，天津　300222
3. 中国农业大学信息与电气工程学院，北京　100083
4. 国网天津市电力公司综合服务中心，天津　300010

收稿日期:2024-06-02 出版日期:2024-12-28 发布日期:2024-12-27
作者简介:陈庆斌（2000—），男，硕士研究生，从事新能源功率预测、综合能源系统研究，E-mail：c1185955913@163.com
杨耿煌（1978—），男，通信作者，博士，教授，从事智能信息处理、电力系统研究，E-mail：ygh@tute.edu.cn
基金资助:
国家重点研发计划资助项目（基于数值天气预测高频更新的风电/光伏日内功率及供电保障能力预测技术，2022YFB2403002）。

Short Term Photovoltaic Power Combination Prediction Method Based on Similar Day Selection and Data Reconstruction

Qingbin CHEN¹(), Genghuang YANG¹^,²(), Liqing GENG¹^,², Juan SU³, Jingsheng SUN⁴

1. School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
2. Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin 300222, China
3. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
4. State Grid Tianjin Electric Power Company Comprehensive Service Center, Tianjin 300010, China

Received:2024-06-02 Online:2024-12-28 Published:2024-12-27
Supported by:
This work is supported by National Key Research and Development Program of China (Wind/Photovoltaic Intraday Power and Power Supply Guarantee Capability Prediction Technology Based on High-Frequency Updates of Numerical Weather Prediction, No.2022YFB2403002).

摘要/Abstract

摘要：

针对光伏功率随机性较强等问题，提出了一种基于相似日选取和数据重构的短期光伏功率组合预测方法。首先，利用核模糊C均值算法对光伏功率进行聚类分析，通过最大信息系数提取主要影响特征；其次，结合合作博弈思想计算预测日和历史日的综合相关系数，挑选相关性较强的历史日构建训练集；然后，利用变分模态分解将光伏功率分解为若干子序列，计算排列熵值并重构为趋势项、低频项和高频项；最后，对趋势项和低频项采用长短期记忆神经网络进行预测，对高频项采用卷积神经网络-双向长短期记忆神经网络-注意力机制模型进行预测，将结果叠加得到最终预测结果。经实例验证，在不同天气条件下，所提模型整体预测误差最小，可有效提高预测精度。

关键词: 光伏功率, 相似日, 变分模态分解, 双向长短期记忆神经网络, 组合预测

Abstract:

A short-term photovoltaic power combination prediction method based on similar day selection and data reconstruction is proposed to address the strong randomness of photovoltaic power. Firstly, clustering analysis of photovoltaic power is performed using the kernel fuzzy C-means algorithm, and the main influencing features are extracted through the maximum information coefficient. Secondly, the cooperative game theory is used to calculate the comprehensive correlation coefficient between the predicted days and the historical days, and the historical days with strong correlation are selected to construct the training set. Then, the variational mode decomposition method is used to decompose the photovoltaic power into several subsequences, and the permutation entropy is calculated and reconstructed into trend, low-frequency, and high-frequency terms. Finally, the long short-term memory neural networks are used to predict the trend and low-frequency items, while the convolutional neural network-bidirectional long short-term memory-attention models are used to predict the high-frequency items. The final prediction result is obtained by overlaying the results. Through practical examples, it has been verified that under different weather conditions, the overall prediction error of the model is the smallest, which can effectively improve the prediction accuracy.

Key words: photovoltaic power, similar days, variational mode decomposition, bidirectional long short term memory neural network, combination prediction

陈庆斌, 杨耿煌, 耿丽清, 苏娟, 孙京生. 基于相似日选取和数据重构的短期光伏功率组合预测方法[J]. 中国电力, 2024, 57(12): 71-81.

Qingbin CHEN, Genghuang YANG, Liqing GENG, Juan SU, Jingsheng SUN. Short Term Photovoltaic Power Combination Prediction Method Based on Similar Day Selection and Data Reconstruction[J]. Electric Power, 2024, 57(12): 71-81.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202406005

https://www.electricpower.com.cn/CN/Y2024/V57/I12/71

图/表 12

参考文献 23

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影响特征	晴天	多云	雨天
组件温度	0.506	0.346	0.385
温度	0.280	0.192	0.233
气压	0.423	0.354	0.327
湿度	0.210	0.169	0.234
总辐射	0.703	0.604	0.579
直射辐射	0.688	0.600	0.575
散射辐射	0.395	0.300	0.413

影响特征	晴天	多云	雨天
组件温度	0.506	0.346	0.385
温度	0.280	0.192	0.233
气压	0.423	0.354	0.327
湿度	0.210	0.169	0.234
总辐射	0.703	0.604	0.579
直射辐射	0.688	0.600	0.575
散射辐射	0.395	0.300	0.413

日期	M₁			M₂			M₃			M₄
日期	Q₁	Q₂	Q₃	Q₁	Q₂	Q₃	Q₁	Q₂	Q₃	Q₁	Q₂	Q₃
06-04	0.890	0.979	0.981	0.840	0.977	0.977	0.519	0.514	0.506	1.312	1.542	1.550
06-05	0.891	0.962	0.964	0.950	0.975	0.977	0.519	0.535	0.532	2.345	2.456	2.457
06-08	0.955	0.955	0.958	0.908	0.938	0.938	0.585	0.593	0.605	1.848	1.877	1.890
06-10	0.839	0.979	0.979	0.720	0.987	0.985	0.628	0.603	0.602	0.931	1.363	1.362
06-15	0.977	0.995	0.996	0.961	0.996	0.996	0.570	0.490	0.485	2.505	2.478	2.474
06-16	0.980	0.998	0.998	0.952	0.998	0.998	0.512	0.472	0.469	0.609	0.599	0.596
06-27	0.979	0.999	0.999	0.950	0.999	0.999	0.600	0.456	0.454	2.529	2.454	2.452
06-30	0.977	0.995	0.995	0.947	0.994	0.994	0.417	0.477	0.473	2.339	2.464	2.460
07-01	0.941	0.959	0.959	0.865	0.966	0.965	0.442	0.502	0.500	2.248	2.427	2.424
07-14	0.969	0.999	1.000	0.914	0.999	0.999	0.871	0.938	0.927	2.734	2.915	2.905
07-18	0.944	0.899	0.905	0.880	0.937	0.933	0.867	0.813	0.836	2.062	2.049	2.068
07-19	0.968	0.958	0.963	0.937	0.941	0.947	0.854	0.895	0.907	0.369	0.387	0.391
07-20	0.948	0.934	0.939	0.926	0.941	0.948	0.838	0.816	0.838	0.032	0.015	0.022
07-24	0.966	0.975	0.976	0.916	0.973	0.970	0.852	0.899	0.906	2.711	2.823	2.828
07-27	0.969	1.000	1.000	0.924	0.999	1.000	0.888	0.975	0.981	2.779	2.972	2.979
07-28	0.932	0.975	0.977	0.989	0.973	0.977	0.862	0.895	0.905	0.786	0.873	0.881

日期	M₁			M₂			M₃			M₄
日期	Q₁	Q₂	Q₃	Q₁	Q₂	Q₃	Q₁	Q₂	Q₃	Q₁	Q₂	Q₃
06-04	0.890	0.979	0.981	0.840	0.977	0.977	0.519	0.514	0.506	1.312	1.542	1.550
06-05	0.891	0.962	0.964	0.950	0.975	0.977	0.519	0.535	0.532	2.345	2.456	2.457
06-08	0.955	0.955	0.958	0.908	0.938	0.938	0.585	0.593	0.605	1.848	1.877	1.890
06-10	0.839	0.979	0.979	0.720	0.987	0.985	0.628	0.603	0.602	0.931	1.363	1.362
06-15	0.977	0.995	0.996	0.961	0.996	0.996	0.570	0.490	0.485	2.505	2.478	2.474
06-16	0.980	0.998	0.998	0.952	0.998	0.998	0.512	0.472	0.469	0.609	0.599	0.596
06-27	0.979	0.999	0.999	0.950	0.999	0.999	0.600	0.456	0.454	2.529	2.454	2.452
06-30	0.977	0.995	0.995	0.947	0.994	0.994	0.417	0.477	0.473	2.339	2.464	2.460
07-01	0.941	0.959	0.959	0.865	0.966	0.965	0.442	0.502	0.500	2.248	2.427	2.424
07-14	0.969	0.999	1.000	0.914	0.999	0.999	0.871	0.938	0.927	2.734	2.915	2.905
07-18	0.944	0.899	0.905	0.880	0.937	0.933	0.867	0.813	0.836	2.062	2.049	2.068
07-19	0.968	0.958	0.963	0.937	0.941	0.947	0.854	0.895	0.907	0.369	0.387	0.391
07-20	0.948	0.934	0.939	0.926	0.941	0.948	0.838	0.816	0.838	0.032	0.015	0.022
07-24	0.966	0.975	0.976	0.916	0.973	0.970	0.852	0.899	0.906	2.711	2.823	2.828
07-27	0.969	1.000	1.000	0.924	0.999	1.000	0.888	0.975	0.981	2.779	2.972	2.979
07-28	0.932	0.975	0.977	0.989	0.973	0.977	0.862	0.895	0.905	0.786	0.873	0.881

日期	M₁		M₂		M₃		M₄
日期	整体相关系数	排序	整体相关系数	排序	整体相关系数	排序	整体相关系数	排序
06-04	2.850	12	2.794	13	1.539	12	4.404	11
06-05	2.817	14	2.902	8	1.586	10	7.258	7
06-08	2.868	10	2.784	14	1.783	9	5.615	10
06-10	2.797	15	2.692	16	1.833	8	3.656	12
06-15	2.968	4	2.953	1	1.545	11	7.457	4
06-16	2.976	2	2.948	2	1.453	14	1.804	14
06-27	2.977	1	2.948	3	1.510	13	7.435	5
06-30	2.967	6	2.935	5	1.367	16	7.263	6
07-01	2.859	11	2.796	12	1.444	15	7.099	8
07-14	2.968	5	2.912	7	2.736	2	8.554	2
07-18	2.748	16	2.750	15	2.516	6	6.179	9
07-19	2.889	8	2.825	10	2.656	5	1.147	15
07-20	2.821	13	2.815	11	2.492	7	0.069	16
07-24	2.917	7	2.859	9	2.657	4	8.362	3
07-27	2.969	3	2.923	6	2.844	1	8.730	1
07-28	2.884	9	2.939	4	2.662	3	2.540	13

基于相似日选取和数据重构的短期光伏功率组合预测方法

Short Term Photovoltaic Power Combination Prediction Method Based on Similar Day Selection and Data Reconstruction

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图/表 12

参考文献 23

相关文章 15

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Metrics

天气	相似日个数	LSTM模型			CNN-BiLSTM- ATTENTION模型
天气	相似日个数	e_MA/ kW	e_RMS/ kW	e_MAP	e_MA/ kW	e_RMS/ kW	e_MAP
晴天	5	3.103	3.815	0.121	3.194	3.774	0.125
	7	3.182	4.033	0.114	2.079	2.538	0.108
	9	2.814	3.452	0.124	2.389	2.736	0.115
	11	2.546	2.886	0.115	2.217	2.502	0.081
多云	5	6.115	7.110	0.294	5.983	6.952	0.271
	7	4.920	6.040	0.226	4.669	5.757	0.218
	9	5.280	6.445	0.239	4.876	6.025	0.219
	11	5.604	6.703	0.253	5.182	6.283	0.233
雨天	5	5.045	6.426	0.335	5.001	6.388	0.324
	7	5.235	6.477	0.337	5.124	6.545	0.333
	9	5.542	6.637	0.421	5.346	6.850	0.334
	11	5.873	6.838	0.454	5.692	7.220	0.356

k	中心频率
3	1.46×10^–4	0.05	0.28
4	4.02×10^–5	0.03	0.12	0.37
5	3.78×10^–5	0.02	0.07	0.23	0.38
6	1.44×10^–5	0.02	0.05	0.12	0.28	0.39
7	1.52×10^–5	0.02	0.05	0.10	0.28	0.38	0.44
8	1.38×10^–5	0.02	0.05	0.10	0.20	0.29	0.38	0.45
9	1.22×10^–5	0.02	0.05	0.08	0.15	0.23	0.29	0.38	0.45

模型	晴天			模型	多云			模型	雨天
模型	e_MA/kW	e_RMS/kW	e_MAP	模型	e_MA/kW	e_RMS/kW	e_MAP	模型	e_MA/kW	e_RMS/kW	e_MAP
A1	6.551	7.947	0.271	A1	7.306	8.293	0.430	A1	8.991	10.821	0.511
A2	6.168	7.322	0.250	A2	6.818	7.797	0.366	A2	8.942	10.530	0.490
A3	5.436	6.633	0.217	A3	6.595	7.552	0.345	A3	8.232	9.934	0.453
A4	4.152	5.491	0.178	A4	6.423	7.359	0.303	A4	5.252	6.783	0.382
A5	4.897	6.002	0.189	A5	6.425	7.361	0.306	A5	5.376	7.000	0.395
A6	4.757	5.794	0.183	A6	6.558	7.409	0.325	A6	5.458	7.244	0.411
A7	2.546	2.886	0.115	A7	4.920	6.040	0.226	A7	5.045	6.426	0.335
A8	2.217	2.502	0.081	A8	4.669	5.757	0.218	A8	5.001	6.388	0.324
A9	2.726	3.167	0.107	A9	4.686	5.832	0.221	A9	5.109	6.581	0.339
A10	1.768	2.378	0.073	A10	4.380	5.271	0.215	A10	4.837	6.084	0.313

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IMF	晴天	多云	雨天
IMF1	0.950	0.999	0.929
IMF2	0.982	0.983	0.940
IMF3	0.971	0.972	0.997
IMF4	0.964	0.959	0.938
IMF5	0.959	0.957	0.951
IMF6	0.990	0.999	0.998
IMF7	0.898	0.994	0.996
IMF8	0.710	0.896	0.862
IMF9		0.698	0.716

IMF	晴天	多云	雨天
IMF1	0.950	0.999	0.929
IMF2	0.982	0.983	0.940
IMF3	0.971	0.972	0.997
IMF4	0.964	0.959	0.938
IMF5	0.959	0.957	0.951
IMF6	0.990	0.999	0.998
IMF7	0.898	0.994	0.996
IMF8	0.710	0.896	0.862
IMF9		0.698	0.716

模态框（Modal）标题

基于相似日选取和数据重构的短期光伏功率组合预测方法

Short Term Photovoltaic Power Combination Prediction Method Based on Similar Day Selection and Data Reconstruction

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