Grey-box modeling for photo-voltaic power systems using dynamic neural-networks

Naji Al-Messabi, Cindy Goh, Yun Li

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

2 Citations (Scopus)
8 Downloads (Pure)

Abstract

There exists various ways of modeling and forecasting photo-voltaic (PV) systems. These methods can be categorized, in board-way, under either definite equations models (white or clear-box) or heuristic data-driven artificial intelligence models (black-box). The two directions of modeling pose a number of drawbacks. To benefit from both worlds, this paper proposes a novel method where clear-box model is extended to a grey-box model by modeling uncertainities using focused time-delay neural network models. The grey-box or semi-definite model was shown to exhibit enhanced forecasting capabilities.

Original languageEnglish
Title of host publication2017 Ninth Annual IEEE Green Technologies Conference (GreenTech)
Place of PublicationPiscataway, NJ.
PublisherIEEE
Pages267-270
Number of pages4
ISBN (Electronic)9781509045358
DOIs
Publication statusPublished - 9 May 2017
Event9th Annual IEEE Green Technologies Conference, GreenTech 2017 - Denver, United States
Duration: 29 Mar 201731 Mar 2017

Conference

Conference9th Annual IEEE Green Technologies Conference, GreenTech 2017
CountryUnited States
CityDenver
Period29/03/1731/03/17

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Keywords

  • grey-box modelling
  • neural networks
  • photovoltaic
  • renewable energy

Cite this

Al-Messabi, N., Goh, C., & Li, Y. (2017). Grey-box modeling for photo-voltaic power systems using dynamic neural-networks. In 2017 Ninth Annual IEEE Green Technologies Conference (GreenTech) (pp. 267-270). [7923969] Piscataway, NJ.: IEEE. https://doi.org/10.1109/GreenTech.2017.45