Method for short-term predicting of photovoltaic generation power on the basis of similar day feature classification and extreme learning machine

A technology of photovoltaic power generation and extreme learning machine, applied in the field of solar photovoltaic power generation, can solve problems such as uncontrollability and uncertainty, and achieve the effect of good short-term prediction accuracy

Active Publication Date: 2013-03-27
ZHEJIANG EIFESUN ENERGY TECH
View PDF2 Cites 29 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the intermittent, uncertain and uncontrollable nature of photovoltaic power generation, when a large-scale, high-capacity photovoltaic power generation system is connected to the grid, it will pose a major challenge to the safe operation of the public grid

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 short-term predicting of photovoltaic generation power on the basis of similar day feature classification and extreme learning machine
  • Method for short-term predicting of photovoltaic generation power on the basis of similar day feature classification and extreme learning machine
  • Method for short-term predicting of photovoltaic generation power on the basis of similar day feature classification and extreme learning machine

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] A short-term prediction method for photovoltaic power generation, comprising the following steps:

[0037] Step 1: Classification and sorting of power generation data: download the temperature, atmospheric pressure, humidity, wind speed data, and power generation data of historical dates from the server. Classify historical data according to the season and day type of the historical day. Season: spring, summer, autumn and winter; day type: sunny, cloudy, rainy.

[0038] Step 2: Download meteorological data: Obtain the meteorological type, temperature, atmospheric pressure, humidity, and wind speed of the relevant period of the forecast day according to the forecast data of the meteorological station.

[0039] Step 3: Preliminarily screen out similar days with similar factors according to the forecast day season, weather type, and temperature.

[0040] Step 4: Calculate the difference degree of daily characteristics: set the weather characteristic sequence (temperature...

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 relates to a method for short-term predicting of photovoltaic generation power on the basis of similar day feature classification and extreme learning machine, belongs to the technical field of photovoltaic station power generation and aims to predict output power of a photovoltaic power generation system. The method includes: firstly, based on meteorological data of a public weather forecast network, subjecting meteorological data and photovoltaic generation system capacity to similar day feature classification according to meteorological features such as season and day type and according to photovoltaic generation power features; secondly, applying a single hidden layer neural network based on extreme learning machine algorithm as a forecast model, and applying selected similar day data as a training sample to train the single hidden layer neural network in the extreme learning machine algorithm; and thirdly, applying known capacity sequence, maximum air temperature and minimum air temperature of similar day forecast periods of most similar forecast days, and maximum air temperature and minimum air temperature of precast periods of the precast days as neural network input, and predicting generating power of a photovoltaic station in future three hours. The requirement of the method for devices is low, a predicting model is highly targeted at regions, and the method is easy to implement and high in precision.

Description

technical field [0001] The invention belongs to the technical field of solar photovoltaic power generation, and relates to a method for short-term forecasting of photovoltaic power generation. Background technique [0002] Solar photovoltaic power generation has the characteristics of low energy density, intermittent, and uncertainty, especially the output power is closely related to meteorological conditions, making its power generation characteristics very different from conventional power. Photovoltaic power grid connection is an important form of large-scale and efficient utilization of photovoltaic power generation. Due to the intermittent, uncertain and uncontrollable nature of photovoltaic power generation, when a large-scale, high-capacity photovoltaic power generation system is connected to the grid, it will pose a major challenge to the safe operation of the public grid. For this reason, if the power generated by the photovoltaic power generation system can be pre...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/02
Inventor 刘士荣张晓东姜碧光胡浙东吴舜裕李松峰
Owner ZHEJIANG EIFESUN ENERGY TECH
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