Photovoltaic annual generating capacity calculation method based on machine learning

A technology of annual power generation and calculation method, applied in the field of photovoltaics, can solve problems such as rough simulation, achieve the effect of improving stability and reducing systematic errors

Active Publication Date: 2019-08-09
旻投电力发展有限公司
View PDF6 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the deficiencies of the prior art, the present invention provides a machine learning-based calculation method for photovoltaic annual power generation, which has the advantages of high efficiency and solves the problem that the current mainstream photovoltaic power plant simulation software performs rough simulations

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 annual generating capacity calculation method based on machine learning
  • Photovoltaic annual generating capacity calculation method based on machine learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0032] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0033] see Figure 1-2 , a method for calculating annual photovoltaic power generation based on machine learning, including the following steps:

[0034] S1: Prediction of irradiance using a time series correlation model:

[0035] a1: Collect the monthly measured meteorological data of all weather stations in the area where the power station is located for 10 years, average, and use the ARp order autoregressive process to fit:

[0036] ;

[0037] a2: Calculate the parameter terms through simulation and predict the ...

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 the technical field of photovoltaics, and discloses a photovoltaic annual generating capacity calculation method based on machine learning, which comprises the following steps: S1, applying a time sequence correlation model to predict irradiance: a1, collecting monthly actual measurement meteorological data of all meteorological stations in a region where a power station is located in history of 10 years, averaging the data, and fitting the data by utilizing an ARp-order autoregressive process; a2, calculating parameter items through simulation, and predicting the monthly average radiation value and temperature of the next year; a3, calculating an illumination intensity distribution curve of the photovoltaic power station in the past year, namely the ratio of illumination time to annual illumination time under each illumination intensity; s2, predicting the generating capacity by utilizing a sample plate assembly method: b1, taking ten sample plate assemblies from different matrixes of a power station, and determining an IV curve and a temperature coefficient of the sample plate assemblies in a laboratory; the photovoltaic annual generating capacity calculation method based on machine learning can solve the problem that simulation carried out by existing mainstream photovoltaic power station simulation software is rough at present.

Description

technical field [0001] The invention relates to the field of photovoltaic technology, in particular to a method for calculating annual photovoltaic power generation based on machine learning. Background technique [0002] With the gradual exhaustion and withering of non-renewable energy sources such as petroleum and coal and the increasing pollution to the environment, photovoltaic power generation has become a major development trend in the power industry. However, with the increasing number of photovoltaic power plants across the country, the installed capacity of distributed photovoltaic power plants is also expanding, resulting in the continuous expansion of the impact of the grid-connected operation of photovoltaic power plants on the power grid. [0003] From the perspective of power grid dispatching and improving power quality of the power grid, accurate photovoltaic power generation forecasting can provide data support for the power grid dispatching department when p...

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): G06Q10/04G06Q50/06G06N20/00
CPCG06Q10/04G06Q50/06G06N20/00Y04S10/50Y02E40/70
Inventor 莫继才肖博莫绍凡虞立军张素君
Owner 旻投电力发展有限公司
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