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基于宽度学习系统的光伏发电功率超短期预测
作者:
作者单位:

1.电力传输与功率变换控制教育部重点实验室(上海交通大学),上海市 200240;2.国家电网公司华东分部,上海市 200120;3.中国电力科学研究院有限公司(南京),江苏省南京市 210003

摘要:

近年来,深度学习被应用于光伏发电预测中,体现出预测精度较高的优点,但也存在训练耗时等问题。对此,提出了一种基于自组织映射与宽度学习系统的光伏发电功率超短期预测模型。首先,采用自组织映射对各时刻的光伏数据进行精细化聚类,提取不同时段与气象条件下的出力波动特征;然后,构建基于宽度学习系统的光伏发电功率多步长预测模型,在网络宽度上扩展节点数目,并通过求解矩阵伪逆训练神经网络,在保证较强高维数据拟合能力的同时,具有较高的计算效率;最后,采用实际光伏发电数据进行算例分析,通过与常用的光伏发电超短期预测方法进行比较,验证所提出的方法在预测精度与训练效率上的优越性。

关键词:

基金项目:

国家自然科学基金资助项目(52077136)。

通信作者:

作者简介:

周楠(1997—),男,硕士研究生,主要研究方向:新能源发电预测。E-mail:zhounan2015@sjtu.edu.cn
徐潇源(1989—),男,通信作者,助理教授,主要研究方向:电力系统不确定性分析、电力系统优化运行。E-mail:xuxiaoyuan@sjtu.edu.cn
严正(1964—),男,教授,博士生导师,主要研究方向:电力系统优化运行、电力系统稳定分析及智能电网。E-mail:yanz@sjtu.edu.cn


Ultra-short-term Forecasting of Photovoltaic Power Generation Based on Broad Learning System
Author:
Affiliation:

1.Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education (Shanghai Jiao Tong University), Shanghai 200240, China;2.East China Branch of State Grid Corporation of China, Shanghai 200120, China;3.China Electric Power Research Institute (Nanjing), Nanjing 210003, China

Abstract:

In recent years, deep learning has been applied to photovoltaic (PV) power forecasting and shows the advantage of high accuracy. However, there also exist problems such as time-consuming training. In order to handle these problems, an ultra-short-term forecasting model of PV power generation based on the self-organizing map (SOM) and broad learning system (BLS) is proposed. Firstly, the SOM is applied to cluster the PV data at each moment to extract the fluctuation characteristics during different periods and under different meteorological conditions. Secondly, a multi-step forecasting model of PV power generation based on BLS is constructed, which increases the number of neurons in width and is trained by calculating the pseudoinverse matrix, thus guaranteeing strong capability for fitting high-dimensional data while keeping high computation efficiency. Finally, the actual PV power generation data are used to carry out the case study. By comparing with commonly used ultra-short-term forecasting methods, the superiority of the proposed method in terms of forecasting accuracy and training efficiency is verified. This work is supported by National Natural Science Foundation of China (No. 52077136).

Keywords:

Foundation:
引用本文
[1]周楠,徐潇源,严正,等.基于宽度学习系统的光伏发电功率超短期预测[J].电力系统自动化,2021,45(1):55-64. DOI:10.7500/AEPS20200228002.
ZHOU Nan, XU Xiaoyuan, YAN Zheng, et al. Ultra-short-term Forecasting of Photovoltaic Power Generation Based on Broad Learning System[J]. Automation of Electric Power Systems, 2021, 45(1):55-64. DOI:10.7500/AEPS20200228002.
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  • 收稿日期:2020-02-28
  • 最后修改日期:2020-08-24
  • 录用日期:
  • 在线发布日期: 2021-01-05
  • 出版日期:
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