|
|
|
| Neural network based photovoltaic power generation prediction method based on empirical mode decomposition |
| Sun Xiangsheng, Chen Fangfang, Jia Jian, Chen Hao, Hu Kangfei |
| School of Electrical and Information Engineering, Yunnan University of Nationalities, Kunming 650504 |
|
|
|
|
Abstract As an important part of power production, photovoltaic power generation forecasting has been regarded as an important part of power system planning and operation. Because of the randomness, complexity and susceptibility to external interference of short-term photovoltaic power generation, it is difficult to make accurate analysis and prediction. In this paper, a short-term photovoltaic power generation prediction method based on empirical mode decomposition is proposed. The model is established by training the historical data and weather conditions of similar days given by photovoltaic power plants as input parameters to predict the next day's power generation. This method is suitable for photovoltaic power generation forecasting and can effectively reduce the error, which has a certain reference value.
|
|
Received: 20 December 2018
Published: 19 August 2019
|
|
|
|
| Cite this article: |
|
Sun Xiangsheng,Chen Fangfang,Jia Jian等. Neural network based photovoltaic power generation prediction method based on empirical mode decomposition[J]. Electrical Engineering, 2019, 20(8): 54-58.
|
|
|
|
| URL: |
|
http://dqjs.cesmedia.cn/EN/Y2019/V20/I8/54
|
[1] 欧阳昌裕. 我国新能源发电发展研究报告[N]. 中国电力报, 2012-03-13(002).
[2] 王雨, 苏适, 严玉廷. 基于Kalman滤波和BP神经网络的光伏超短期功率预测模型[J]. 电气技术, 2014, 15(1): 42-46.
[3] 王思睿, 薛云灿, 李彬, 等. 基于BP神经网络的光伏发电预测模型设计[J]. 微处理机, 2016, 37(2): 82-85.
[4] 陆源, 张兆云. 光伏发电系统仿真研究[J]. 电气技术, 2013, 14(8): 1-6, 15.
[5] 迟晨. 光伏发电系统的RBF神经网络预测研究[J]. 电子技术与软件工程, 2018(3): 191.
[6] 李安寿, 陈琦, 王子才, 等. 光伏发电系统功率预测方法综述[J]. 电气传动, 2016, 46(6): 93-96.
[7] 李天云, 赵妍, 季小慧, 等. HHT方法在电力系统故障信号分析中的应用[J]. 电工技术学报, 2005, 20(6): 87-91.
[8] 何敏. 基于Hilbert-Huang变换获得睡眠脑电信号特征[J]. 电脑知识与技术, 2015, 11(20): 161-163.
[9] 杨茂, 陈郁林. 基于EMD分解和集对分析的风电功率实时预测[J]. 电工技术学报, 2016, 31(21): 86-93.
[10] 苗晟, 王威廉, 姚绍文. Hilbert-Huang变换发展历程及其应用[J]. 电子测量与仪器学报, 2014, 28(8): 812-818.
[11] 王守相, 王亚旻, 刘岩, 等. 基于经验模态分解和ELM神经网络的逐时太阳能辐照量预测[J]. 电力自动化设备, 2014, 34(8): 7-12.
[12] 陈明. Matlab神经网络原理与实例讲解[M]. 北京: 清华大学出版社, 2013.
[13] 周政. BP神经网络的发展现状综述[J]. 山西电子技术, 2008(2): 90-92.
[14] 孙帆, 施学勤. 基于Matlab的BP神经网络设计[J]. 计算机与数字工程, 2007, 35(8): 124-126.
[15] 孙文韬. 反向传播神经网络的原理研究及优化方法[J]. 中国科技博览, 2011(32): 511-512.
[16] 于群. 基于EEMD和BP神经网络的短期光伏功率预测模型[D]. 沈阳: 沈阳农业大学, 2017. |
|
|
|
2019, Vol. 20 
