Photovoltaic System Power Generation Forecasting Based on Spiking Neural Network

被引：1
|
作者：
Chen, Tong [1 ]
Sun, Guoqiang [1 ]
Wei, Zhinong [1 ]
Li, Huijie [2 ]
Cheung, Kwok W. [3 ]
Sun, Yonghui [1 ]
机构：
[1] Hohai Univ, Coll Energy & Elect Engn, Nanjing 211100, Jiangsu, Peoples R China
[2] ALSTOM GRID Technol Ctr Co Ltd, Shanghai 201114, Peoples R China
[3] ALSTOM Grid Inc, Redmond, WA 98052 USA
来源：
PROCEEDINGS OF THE 2015 CHINESE INTELLIGENT SYSTEMS CONFERENCE, VOL 1 | 2016年 / 359卷
关键词：
Photovoltaic system; Spiking neural network; Similar day selection algorithm; Power generation forecasting; NEURONS;
D O I：
10.1007/978-3-662-48386-2_59
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A forecasting model based on Spiking neural network (SNN) was proposed to tackle with the problem of the forecasting of photovoltaic system (PVS) power generation. This neural network uses temporal encoding scheme with precise times of spikes, which is closer to the real biological neural system and has powerful computing ability. Considering the main influencing factors such as season types, weather types, sunshine intensity and temperature etc., this model use the method of grey correlation analysis to select similar days. The high accuracy and robust applicability of the proposed forecasting model are verified by the simulation using actual operating data of PVS.
引用
收藏
页码：573 / 581
页数：9
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