Forecasting I-V characteristic of PV modules considering real operating conditions using numerical method and deep learning

Nguyen Duc Tuyen, Le Viet Thinh, Vu Xuan Son Huu, Goro Fujita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The current-voltage (I-V) characteristic plays a dominant role in operating a photovoltaic (PV) system as it provides information about the performance of the system. Since the output quality of PV depends mainly on the solar irradiation and cell temperature, modeling the I-V relationship regarding solar irradiation and cell temperature need to be addressed. In this paper, the long short-term memory (LSTM) model is adopted to forecast the solar irradiation and temperature of a PV module. After that, a PV module model called one-diode model is introduced to identify the I-V characteristic of the PV module, which only employs the data forecasted by the LSTM-based model and the manufactured data. Since this method combines the strengths of two techniques, it solves the uncertainty of meteorological data as well as provides an effective method to model the I-V output quality of the PV module.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on Smart Grids and Energy Systems, SGES 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages544-549
Number of pages6
ISBN (Electronic)9781728185507
DOIs
Publication statusPublished - 2020 Nov
Event2020 International Conference on Smart Grids and Energy Systems, SGES 2020 - Virtual, Perth, Australia
Duration: 2020 Nov 232020 Nov 26

Publication series

NameProceedings - 2020 International Conference on Smart Grids and Energy Systems, SGES 2020

Conference

Conference2020 International Conference on Smart Grids and Energy Systems, SGES 2020
CountryAustralia
CityVirtual, Perth
Period20/11/2320/11/26

Keywords

  • Deep learning
  • I-V characteristic
  • Long short-term memory (LSTM)
  • One-diode model (ODM)
  • Photovoltaic module

ASJC Scopus subject areas

  • Artificial Intelligence
  • Energy Engineering and Power Technology
  • Automotive Engineering
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Transportation

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