Method for predicating output power of photovoltaic power generation system based on BP (Back Propagation) neural network model

A photovoltaic power generation system and BP neural network technology, applied in the field of power systems, can solve problems such as conversion efficiency, differences in installation angles, poor prediction results, and increased difficulty in output model parameter values ​​of photovoltaic power generation systems, so as to improve efficiency and reduce algorithm complexity Accuracy, improved accuracy and generalization

Inactive Publication Date: 2013-07-24
STATE GRID CORP OF CHINA +2
View PDF2 Cites 40 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For systems with different types of photovoltaic power generation units, there are also differences in parameters such as conversion efficiency and installation angle, and it is more difficult to det

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for predicating output power of photovoltaic power generation system based on BP (Back Propagation) neural network model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0022] Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these embodiments are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention Modifications in equivalent forms all fall within the scope defined by the appended claims of this application.

[0023] The idea of ​​the present invention is to use the artificial intelligence neural network to construct a method for predicting the output power of a photovoltaic power generation system, and use the prediction model to predict the amount of photovoltaic power generation at the next moment.

[0024] The process of establishing the prediction model will be described in detail below.

[0025] see figure 1 As shown, the photovoltaic power generation system output powe...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a method for predicating the output power of a photovoltaic power generation system based on a BP (Back Propagation) neural network model. The method comprises the following steps of selecting influencing factors of the output power of the photovoltaic power generation system; generating input vectors according to the historical data of the selected influencing factors, and utilizing the historical data of the output power of the corresponding photovoltaic power generation system as output, thereby obtaining training samples; training a BP neural network by utilizing the training samples, thereby obtaining the trained BP neural network; generating test input vectors according to real data at the to-be-predicated moment of the selected influencing factors, inputting the test input vectors into the trained BP neural network, so that the output is the predicted value of the output power of the photovoltaic power generation system at the to-be predicated moment. According to the method for predicating the output power of the photovoltaic power generation system, modeling prediction is performed; the invention provides a predication method based on the BP neural network; the favorable nonlinear function approximation capability of the BP neural network is utilized, so that the precision and generalization capability of a prediction model are improved.

Description

technical field [0001] The invention belongs to the technical field of power systems, and in particular relates to a method for predicting the output power of a photovoltaic power generation system based on a BP neural network model. Background technique [0002] With the global energy shortage and environmental protection issues becoming more and more prominent, the utilization of renewable energy has attracted extensive attention. As an important form of renewable energy, photovoltaic power generation is one of the power generation methods with the most large-scale development conditions and commercial development prospects in renewable energy, and it has attracted more and more attention. [0003] At present, large-scale photovoltaic power generation systems have been built in large numbers at home and abroad. Hongze Photovoltaic Power Station in Huai'an, Jiangsu Province is located on the beach of Baima Lake in Hongze County. The long-term planned total construction...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06Q10/04G06Q50/06
CPCY04S10/54Y04S10/50
Inventor 李瑞洲徐晓春蒋志成汤同峰
Owner STATE GRID CORP OF CHINA
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap