基于灰色关联分析和GeoMAN模型的光伏发电功率短期预测

doi:10.19595/j.cnki.1000-6753.tces.200538

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (1082 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract

Accurateforecast of photovoltaic (PV) power is important for power system dispatch. A new short-term PV power forecastingmethod is proposed based on grey relational analysis andGeoMAN model. Firstly, grey relational analysis is utilized to analyze spatial correlation among multiple PV stations. And several surrounding PV stations highly related with the target PV station are selected. Then, GeoMAN model is established for dynamically extracting the spatiotemporal feature and external meteorological factors. GeoMAN model adopts encoder and decoder structure. The encoderis utilized to dynamically extract the intra-station feature of the target station and the inter-station spatial feature with the related stations. The decoder is utilized to extract the time feature of input variables. Clearness index and numerical weather prediction (NWP) are finally integrated for short-term PV power forecast. A case study is conducted using data collected from practical PV stations. Study results indicate that the proposed method can achieve higher accuracy compared with long short-term memory (LSTM) model.

Key words： Short-term PV power forecast grey relational analysis GeoMANmodel spatiotemporal correlation attentional mechanism

Received: 25 May 2020

PACS:

TM615

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors

Cite this article:

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.200538 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I11/2298

[1] 赖昌伟, 黎静华, 陈博, 等. 光伏发电出力预测技术研究综述[J]. 电工技术学报, 2019, 34(6): 1201-1217.
Lai Changwei, Li Jinghua, Chen Bo, et al.Review of photovoltaic power output prediction technology[J]. Transactions of China Electrotechnical Society, 2019, 34(6): 1201-1217.
[2] 张雨曼, 刘学智, 严正, 等. 光伏-储能-热电联产综合能源系统分解协调优化运行研究[J]. 电工技术学报, 2020, 35(11): 2372-2386.
Zhang Yuman, Liu Xuezhi, Yan Zheng, et al.Decomposition-coordination based optimization for PV-BESS-CHP integrated energy systems[J]. Transactions of China Electrotechnical Society, 2020, 35(11): 2372-2386.
[3] He Hui, Hu Ran, Zhang Yaning, et al.A power forecasting approach for PV plant based on irradiance index and LSTM[C]//Proceedings of the 37th Chinese Control Conference, Wuhan, China, 2018: 9404-9409.
[4] 黄磊, 舒杰, 姜桂秀, 等. 基于多维时间序列局部支持向量回归的微网光伏发电预测[J]. 电力系统自动化, 2014, 38(5): 19-24.
Huang Lei, ShuJie, Jiang Guixiu, et al. Photovoltaic generation forecast based on multidimensional time-series and local support vector regression in microgrids[J]. Automation of Electric Power Systems, 2014, 38(5): 19-24.
[5] 赵滨滨, 王莹, 王彬, 等. 基于ARIMA时间序列的分布式光伏系统输出功率预测方法研究[J]. 可再生能源, 2019, 37(6): 820-823.
Zhao Binbin, Wang Ying, Wang Bin, et al.Research on output power forecast method of distributed photovoltaic system based on ARIMA time series[J]. Renewable Energy, 2019, 37(6): 820-823.
[6] 焦田利, 章坚民, 李熊,等. 基于空间相关性的大规模分布式用户光伏空间分群方法[J]. 电力系统自动化, 2019, 43(21): 97-105, 162.
Jiao Tianli, Zhang Jianmin, Li Xiong, et al.Spatial clustering method for large-scale distributed user photovoltaics based on spatial correlation[J]. Automation of Electric Power Systems, 2019, 43(21): 97-105, 162.
[7] 于若英, 陈宁, 苗淼, 等. 考虑天气和空间相关性的光伏电站输出功率修复方法[J]. 电网技术, 2017, 41(7): 2229-2236.
Yu Reying, Chen Ning, Miao Miao, et al.A repair method for PV power station output data considering weather and spatial correlations[J]. Power System Technology, 2017, 41(7): 2229-2236.
[8] 阚博文, 刘广一, Khodayar Mahdi, 等. 基于图机器学习的分布式光伏发电预测[J]. 供用电, 2019, 36(11): 20-27.
Kan Bowen, Liu Guangyi, KhodayarM, et al. Distributed photovoltaic generation prediction based on graph machine learning[J]. Distribution &Utilization, 2019, 36(11): 20-27.
[9] 王晶, 黄越辉, 李驰, 等. 考虑空间相关性和天气类型划分的多光伏电站时间序列建模方法[J]. 电网技术, 2020, 44(4): 1376-1384.
Wang Jing, HuangYuehui, Li Chi, et al. Time series modeling method for multi-photovoltaic power stations considering spatial correlation and weather type classification[J]. Power System Technology,2020, 44(4): 1376-1384.
[10] 焦田利. 基于时空关系的广域分布式光伏发电群出力预测关键模型研究[D]. 杭州: 杭州电子科技大学, 2019.
[11] 路宽, 赵岩, 王昕, 等. 一种基于编码解码长短期记忆网络的短期风电功率预测方法: 中国, CN108711847B[P].2019-06-04.
[12] 钟建林, 何友, 王红星. 基于多频小波分析和D-S推理的电路故障诊断[J]. 电工技术学报, 2010, 25(8): 180-184, 192.
ZhongJianlin, HeYou, Wang Hongxing. Circuit fault diagnosis based on multi-frequency wavelet analysis and D-S reasoning[J]. Transactions of China Electrotechnical Society, 2010, 25(8): 180-184, 192.
[13] Liang Yuxuan, KeSongyu, ZhangJunbo, et al. GeoMAN: multi-level attention networks for Geo-sensory time series prediction[C]//Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, 2018: 3428-3434.
[14] 李冬辉, 尹海燕, 郑博文, 等. 改进的LSTM方法在冷水机组传感器故障检测中的应用[J]. 电工技术学报, 2019, 34(11): 2324-2332.
Li Donghui, Yin Haiyan, Zheng Bowen, et al.Application of improved LSTM method in sensor fault detection of the chiller[J]. Transactions of China Electrotechnical Society, 2019, 34(11): 2324-2332.
[15] 朱文立, 张利, 杨明, 等. 考虑日周期性影响的光伏功率爬坡事件非精确概率预测[J]. 电力系统自动化, 2019, 43(20): 31-40.
Zhu Wenli, Zhang Li, Yang Ming, et al.Imprecise probabilistic prediction of photovoltaic power ramp event considering daily periodic effect[J]. Automation of Electric Power Systems, 2019, 43(20): 31-40.
[16] 谢恩哲. 考虑气象要素的光伏预测模型研究[D]. 哈尔滨: 哈尔滨理工大学, 2015.
[17] 吉锌格, 李慧, 刘思嘉, 等. 基于MIE-LSTM的短期光伏功率预测[J]. 电力系统保护与控制, 2020, 48(7): 50-57.
JiXinge, Li Hui, Liu Sijia, et al. Short-term photovoltaic power forecasting based on MIE-LSTM[J]. Power System Protection and Control,2020, 48(7): 50-57.
[18] 王晨, 寇鹏. 基于卷积神经网络和简单循环单元集成模型的风电场内多风机风速预测[J]. 电工技术学报, 2020, 35(13): 2723-2735.
Wang Chen, Kou Peng.Wind speed forecasts of multiple wind turbines in a wind farm based on integration model built by convolutional neural network and simple recurrent unit[J]. Transactions of China Electrotechnical Society, 2020, 35(13): 2723-2735.