Ultra-short-term Prediction of Photovoltaic Power Generation Based on Multi-channel Input and PCNN-BiLSTM

被引：0
|
作者：
Bi G. [1 ]
Zhao X. [1 ]
Chen C. [1 ]
Chen S. [1 ]
Li L. [1 ]
Xie X. [1 ]
Luo Z. [1 ]
机构：
[1] School of Electric Power Engineering, Kunming University of Science and Technology, Yunnan Province, Kunming
来源：
Dianwang Jishu/Power System Technology | 2022年 / 46卷 / 09期
基金：
中国国家自然科学基金;
关键词：
bi-directional long/short-term memory; multi-channel input; parallel convolutional neural network; photovoltaic power generation; power prediction;
D O I：
10.13335/j.1000-3673.pst.2022.0917
中图分类号：
学科分类号：
摘要：
Photovoltaic (PV) power generation has high uncertainties due to the randomness and instability nature of solar energy and meteorological parameters. Hence, accurate PV power prediction is essential in the operation of PV power plants for the short-term dispatches and power generation schedules. In this paper, a combined prediction method based on multi-modal decomposition, multi-channel input, parallel convolutional neural network and bi-directional long/short-term memory neural network is proposed for the ultra-short-term PV power generation prediction in different weather types. First, it is determined by the correlation analysis algorithm that radiation and temperature are the two environmental variables that contribute the most to the PV power generation, and the annual data is divided into four types based on the meteorological factors and the fluctuation characteristics of PV power generation; Secondly, the CEEMDAN, SSD and VMD are used to decompose the radiation, temperature and PV power generation under various weather types, in order to reduce the complexity and non-stationary of the original data and realize the complementation between the modal component regular patterns under different modes; Finally, a combined prediction model is built based on the PCNN and the BiLSTM, The PCNN is uesd to extract different depth features, and the features output by PCNN are fused and input into the BiLSTM, which is establish the temporal feature relationship between the historical data, learn the forward and reverse laws between the historical data. The final PV power generation prediction results are obtained based on the analysis of the spatiotemporal correlation. The experimental results show that the proposed combined prediction has high accuracy and stability in the ultra-short-term PV power generation prediction, outperforming the other deep learning methods. © 2022 Power System Technology Press. All rights reserved.
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页码：3463 / 3476
页数：13
相关论文
共 26 条
  • [1] CHANG Yekui, LIU Rao, BA Yu, Frequency control mode and performance assessment in UHV grid with high proportion of renewable energy resources[J], Power System Technology, 43, 2, pp. 621-629, (2019)
  • [2] LI Meicheng, MEI Wenming, ZHANG Lingkang, Research on multi-energy microgrid scheduling optimization model based on renewable energy uncertainty[J], Power System Technology, 43, 4, pp. 1260-1270, (2019)
  • [3] HUANG Yuwei, PENG Daogang, YAO Jun, Ultra-short-term photovoltaic power forecast of TD-BP neural network based on SSA and K-means[J], Acta Energiae Solaris Sinica, 42, 4, pp. 229-238, (2021)
  • [4] KORKMAZ D, SolarNet ： a hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting[J], Applied Energy, 300, (2021)
  • [5] SUN Rongfu, WANG Longyang, WANG Yulin, Ultra-short-term prediction of photovoltaic power generation based on digital twins[J], Power System Technology, 45, 4, pp. 1258-1264, (2021)
  • [6] QIAO Ying, SUN Rongfu, DING Ran, Distributed photovoltaic station cluster short-term power forecasting part II ： gridding prediction[J], Power System Technology, 45, 6, pp. 2210-2218, (2021)
  • [7] MENG Anbo, XU Xuancong, CHEN Jiaming, Ultra short term photovoltaic power prediction based on reinforcement learning and Combined deep learning model[J], Power System Technology, 45, 12, pp. 4721-4728, (2021)
  • [8] MENG Anbo, CHEN Jiaming, LI Zhanlian, Short-term photovoltaic power generation prediction based on similar day theory and CSO-WGPR[J], High Voltage Engineering, 47, 4, pp. 1176-1184, (2021)
  • [9] Hao YIN, OU Zuhong, CHEN De, Ultra-short-term wind power prediction based on two-layer mode decomposition and cascaded deep learning[J], Power System Technology, 44, 2, pp. 445-453, (2020)
  • [10] WANG Hao, YANG Dongsheng, ZHOU Bowen, Fault diagnosis of multi-terminal HVDC transmission line based on parallel convolutional neural network[J], Automation of Electric Power Systems, 44, 12, pp. 84-92, (2020)
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