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

Al-Messabi, Naji and Goh, Cindy and Li, Yun; (2017) Grey-box modeling for photo-voltaic power systems using dynamic neural-networks. In: 2017 Ninth Annual IEEE Green Technologies Conference (GreenTech). IEEE, USA, pp. 267-270. ISBN 9781509045358 (https://doi.org/10.1109/GreenTech.2017.45)

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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.

ORCID iDs

Al-Messabi, Naji, Goh, Cindy and Li, Yun ORCID logoORCID: https://orcid.org/0000-0002-6575-1839;
  • Item type:Book Section
    ID code:65275
    Dates:
    Date
    Event
    9 May 2017
    Published
    15 January 2017
    Accepted
    Notes:© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering
    Depositing user: Pure Administrator
    Date deposited:27 Aug 2018 12:12
    Last modified:11 Nov 2024 15:14
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/65275
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