Photovoltaic generation power prediction method based on self-learning radial basis function

A technology of photovoltaic power generation and radial core, applied in forecasting, data processing applications, instruments, etc., can solve the problems of photovoltaic power generation uncertainty, uncontrollable power grid security, stability and economic operation, etc., to improve accuracy and optimize power grid Scheduling effect

Inactive Publication Date: 2014-07-16
STATE GRID CORP OF CHINA +2
View PDF4 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

With the continuous improvement of the grid-connected scale of new energy, the uncertainty and uncontrollability of ph

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
  • Photovoltaic generation power prediction method based on self-learning radial basis function
  • Photovoltaic generation power prediction method based on self-learning radial basis function
  • Photovoltaic generation power prediction method based on self-learning radial basis function

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0042] The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described here are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0043] A photovoltaic power generation prediction method based on self-learning radial basis kernel function, including:

[0044] Obtain the steps of obtaining the SVM model through model training;

[0045] And the step of inputting the data required for photovoltaic power generation prediction into the SVM model obtained by the above training to obtain the prediction result.

[0046] Among them, the steps to obtain the SVM model through model training include:

[0047] Step 101: Input basic data for model training;

[0048] Step 102: Preprocessing the above-mentioned input basic training data;

[0049] Step 103: SVM classifier training;

[0050] Step 104: Obtain an SVM prediction m...

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 photovoltaic generation power prediction method based on a self-learning radial basis function. The photovoltaic generation power prediction method based on the self-learning radial basis function comprises the steps that model training is conducted to enable an SVM model to be obtained; data required by photovoltaic generation power prediction are input into the SVM model obtained through training, so that a prediction result is obtained. Key information is provided for new energy power generation real-time scheduling, a new energy power generation plan, new energy power generation capability evaluation and wind curtailment power estimation by predicting the photovoltaic generation power generated during photovoltaic generation. The ultra-short-term prediction accuracy is effectively improved by the adoption of a composite data source, and thus high-accuracy short-term photovoltaic generation power prediction is achieved.

Description

technical field [0001] The present invention relates to the technical field of prediction of photovoltaic power generation in the process of new energy power generation, that is, a method for predicting photovoltaic power generation power based on self-learning radial basis kernel function, and specifically relates to a self-learning radial basis kernel function based on a composite data source Short-term prediction method of photovoltaic power generation based on support vector machine. Background technique [0002] Most of the large-scale new energy bases are located in the "Three North Regions" (Northwest, Northeast, and North China). Large-scale new energy bases are generally far away from the load center, and their power needs to be transmitted to the load center for consumption through long-distance and high-voltage transmission. Due to the intermittence, randomness and volatility of wind and light resources, the output of wind power and photovoltaic power generation i...

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
CPCY04S10/50
Inventor 路亮汪宁渤靳丹师建中崔刚贾怀森张鹏
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