E3S Web Conf.
Volume 185, 20202020 International Conference on Energy, Environment and Bioengineering (ICEEB 2020)
|Number of page(s)||5|
|Section||Energy Engineering and Power System|
|Published online||01 September 2020|
A Convolutional Neural Network for Regional Photovoltaic Generation Point Forecast
1 State Grid Shandong Electric Power Company, Jinan, Shandong 250021 China
2 Liaocheng Power Supply Company of State Grid Shandong Electric Power Company, Liaocheng Shandong, 252000 China
3 Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education, Shandong University, Jinan, Shandong 250061 China
* Corresponding author’s e-mail: Zxk200000@126.com
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.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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