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

Naji Al-Messabi; Cindy Goh; Yun Li

doi:10.1109/GreenTech.2017.45

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

Naji Al-Messabi, Cindy Goh, Yun Li

Faculty Of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

3 Citations (Scopus)

36 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 language	English
Title of host publication	2017 Ninth Annual IEEE Green Technologies Conference (GreenTech)
Place of Publication	Piscataway, NJ.
Publisher	IEEE
Pages	267-270
Number of pages	4
ISBN (Electronic)	9781509045358
DOIs	https://doi.org/10.1109/GreenTech.2017.45
Publication status	Published - 9 May 2017
Event	9th Annual IEEE Green Technologies Conference, GreenTech 2017 - Denver, United States Duration: 29 Mar 2017 → 31 Mar 2017

Conference

Conference	9th Annual IEEE Green Technologies Conference, GreenTech 2017
Country/Territory	United States
City	Denver
Period	29/03/17 → 31/03/17

Keywords

grey-box modelling
neural networks
photovoltaic
renewable energy

Access to Document

10.1109/GreenTech.2017.45

Al-Messabi-etal-GreenTech-2017-Grey-box-modeling-for-photo-voltaic-power-systems-using-dynamic-neural-networksAccepted author manuscript, 473 KB

http://eprints.gla.ac.uk/143681/

Cite this

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title = "Grey-box modeling for photo-voltaic power systems using dynamic neural-networks",

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.",

keywords = "grey-box modelling, neural networks, photovoltaic, renewable energy",

author = "Naji Al-Messabi and Cindy Goh and Yun Li",

note = "{\textcopyright} 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.; 9th Annual IEEE Green Technologies Conference, GreenTech 2017 ; Conference date: 29-03-2017 Through 31-03-2017",

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language = "English",

pages = "267--270",

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AU - Al-Messabi, Naji

AU - Goh, Cindy

AU - Li, Yun

N1 - © 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.

PY - 2017/5/9

Y1 - 2017/5/9

N2 - 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.

AB - 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.

KW - grey-box modelling

KW - neural networks

KW - photovoltaic

KW - renewable energy

UR - https://www.scopus.com/pages/publications/85019750194

U2 - 10.1109/GreenTech.2017.45

DO - 10.1109/GreenTech.2017.45

M3 - Conference contribution book

AN - SCOPUS:85019750194

SP - 267

EP - 270

BT - 2017 Ninth Annual IEEE Green Technologies Conference (GreenTech)

PB - IEEE

CY - Piscataway, NJ.

T2 - 9th Annual IEEE Green Technologies Conference, GreenTech 2017

Y2 - 29 March 2017 through 31 March 2017

ER -

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

Abstract

Conference

Keywords

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