A Convolutional Neural Network for Regional Photovoltaic Generation Point Forecast

被引：4

作者：

Zhang, Xuekai ^{[1
]}

Yang, Yanyong ^{[2
]}

Wang, Huaying ^{[2
]}

Zhao, Feitao ^{[2
]}

Yan, Fangqing ^{[3
]}

Wang, Mengxia ^{[3
]}

机构：

[1] State Grid Shandong Elect Power Co, Jinan 250021, Shandong, Peoples R China

[2] State Grid Shandong Elect Power Co, Liaocheng Power Supply Co, Liaocheng 252000, Shandong, Peoples R China

[3] Shandong Univ, Minist Educ, Key Lab Power Syst Intelligent Dispatch & Control, Jinan 250061, Shandong, Peoples R China

来源：

2020 INTERNATIONAL CONFERENCE ON ENERGY, ENVIRONMENT AND BIOENGINEERING (ICEEB 2020) | 2020年 / 185卷

关键词：

POWER OUTPUT; PREDICTION;

D O I：

10.1051/e3sconf/202018501079

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

As the rapid growth of photovoltaic (PV) generation capacity, the form of regional PV power integrated by multiple PV plants is becoming more and more common. The changing law of regional PV power is of great significance to control the operation of the power system. This paper presents a novel regional PV power point forecast method that uses the convolutional neural network (CNN) model. In the method, the structure of CNN is applied to extract the nonlinear features between the input data and regional PV power. The forecast of regional PV power in a real power grid is carried out to illustrate the validity of the proposed method. Verification results show that the CNN model can provide more accurate point forecast for regional PV power results than the traditional regional PV power forecast methods.

引用

页数：5

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