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基于延迟回声状态网的光伏电池板温度预测方法

范思远 姚显双 曹生现 赵波

范思远, 姚显双, 曹生现, 赵波. 基于延迟回声状态网的光伏电池板温度预测方法. 自动化学报, 2020, 46(12): 2701−2710 doi: 10.16383/j.aas.c200167
引用本文: 范思远, 姚显双, 曹生现, 赵波. 基于延迟回声状态网的光伏电池板温度预测方法. 自动化学报, 2020, 46(12): 2701−2710 doi: 10.16383/j.aas.c200167
Fan Si-Yuan, Yao Xian-Shuang, Cao Sheng-Xian, Zhao Bo. Temperature prediction of photovoltaic panels based on delayed echo state network. Acta Automatica Sinica, 2020, 46(12): 2701−2710 doi: 10.16383/j.aas.c200167
Citation: Fan Si-Yuan, Yao Xian-Shuang, Cao Sheng-Xian, Zhao Bo. Temperature prediction of photovoltaic panels based on delayed echo state network. Acta Automatica Sinica, 2020, 46(12): 2701−2710 doi: 10.16383/j.aas.c200167

基于延迟回声状态网的光伏电池板温度预测方法

doi: 10.16383/j.aas.c200167
基金项目: 国家重点研发计划(2018YFB1500800), 吉林省科技发展计划(20190302079GX), 吉林市科技创新发展计划(201830819)资助
详细信息
    作者简介:

    范思远:东北电力大学博士研究生. 2018年获得东北电力大学仪器科学与技术硕士学位. 主要研究方向为新能源发电系统检测及智能控制.E-mail: fans@neepu.edu.cn

    姚显双:工学博士, 东北电力大学副教授. 主要研究方向为新能源发电系统建模与控制.E-mail: xianshuang_yao@163.com

    曹生现:工学博士, 东北电力大学教授. 主要研究方向为新能源发电测控技术. 本文通信作者.E-mail: csxlb_jl@163.com

    赵波:工学博士, 东北电力大学副教授. 主要研究方向为新能源发电检测技术与自动化装置, 表面污垢监测与控制.E-mail: zhaobo@neepu.edu.cn

Temperature Prediction of Photovoltaic Panels Based on Delayed Echo State Network

Funds: Supported by National Key Research and Development Program of China (2018YFB1500800), Science and Technology Development Program of Jilin Province (20190302079GX), Science and Technology Innovation Development Program of Jilin City (201830819)
  • 摘要: 光伏电池温度变化影响光伏系统输出的稳定性, 精准地预测光伏电池板温度的变化趋势, 对光伏系统智能运行具有重要意义. 为了更好地预测温度的变化趋势, 本文考虑了光伏电池板温度的迟滞效应, 将先前的温度输出作为延迟项引入回声状态网中, 提出了一种基于延迟回声状态网的光伏电池板温度预测模型. 给出一个延迟回声状态网具有回声状态特性的判定条件, 使得预测模型能够稳定地预测光伏电池板温度. 同时, 建立了一套光伏多传感器监测系统, 利用该监测系统采集的数据, 训练和验证模型的准确性. 与回声状态网(Echo state network, ESN), Leaky ESN (Leaky-integrator ESN)和VML ESN (ESN with variable memory length)相比, 仿真结果表明, 本文所提出的延迟回声状态网具有更好的预测性能, 平均绝对百分比误差甚至达到3.45%.
  • 图  1  回声状态网络的结构

    Fig.  1  The structure of echo state network

    图  2  延迟回声状态网络的结构

    Fig.  2  The structure of delayed echo state network

    图  3  光伏电池板多传感器监测系统

    Fig.  3  Multi-sensor monitoring system for PV panel

    图  4  四种方法的预测输出和原始数据

    Fig.  4  The actual output and original data of four methods

    图  5  四种方法的预测误差

    Fig.  5  The prediction error of four methods

    图  6  不同延迟因子和储备池规模优化性能分析

    Fig.  6  The optimization performance analysis of different delay factors and sizes of reservoir

    表  1  监测系统记录的数据

    Table  1  Data recorded by monitoring system

    时间 输入 输出
    光照幅度
    (W/m2)
    环境温度
    (℃)
    风速
    (m/s)
    风向
    (°)
    电池板温度
    (℃)
    8:00 585 25.10 2.2 205 34.53
    8:15 610 26.50 2.0 224 35.79
    8:30 649 26.50 2.1 252 35.62
    8:45 665 27.30 2.9 199 38.60
    $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $
    15:15 699 32.50 2.2 242 45.22
    15:30 518 32.30 2.6 242 41.97
    15:45 311 31.10 3.2 273 38.62
    16:00 159 30.80 2.4 205 34.64
    下载: 导出CSV

    表  2  延迟回声状态网储备池参数

    Table  2  The reservoir parameters of delayed ESN

    储备池规模 谱半径 稀疏度 (%) 训练样本长度
    50 0.82 2 1400
    下载: 导出CSV

    表  3  四种方法的预测精度对比

    Table  3  Comparison of prediction accuracy of four methods

    预测模型 储备池规模 训练 RMSE 测试 RMSE 测试 MAPE (%)
    ESN[10] 50 0.66058 1.4475 6.81
    Leaky ESN[25] 50 0.5922 1.2052 5.27
    VML ESN[29] 50 0.47278 0.7931 4.17
    延迟 ESN 50 0.42802 0.6399 3.45
    下载: 导出CSV

    表  4  延迟回声状态网不同储备池规模性能分析

    Table  4  Delayed ESN performance analysis for different sizes of reservoir

    储备池规模 (N) 样本长度 评价指标 (测试MAPE)
    20 1400 0.0514
    30 1400 0.0495
    40 1400 0.0407
    50 1400 0.0345
    60 1400 0.0352
    70 1400 0.0667
    80 1400 0.0542
    90 1400 0.0571
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-30
  • 录用日期:  2020-07-12
  • 网络出版日期:  2020-12-29
  • 刊出日期:  2020-12-29

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