Artificial neural network for real time modelling of photovoltaic system under partial shading

Maria Carla Di Vincenzo

doi:10.1109/ICSET.2010.5684464

Artificial neural network for real time modelling of photovoltaic system under partial shading

Maria Carla Di Vincenzo

Electronic And Electrical Engineering

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

12 Citations (Scopus)

Abstract

Shading caused by surrounding objects is an important issue for solar energy system design and analysis. In the special case of building integrated photovoltaic (BIPV) systems, the prediction of the partial shading is critical in order to reduce losses due to poor Maximum Power Point Tracking (MPPT). This paper will present a technique that uses Artificial Neural Network to predict the output power from a photovoltaic array in case of partial shading.

Original language	English
Title of host publication	2010 IEEE International Conference on Sustainable Energy Technologies (ICSET)
Place of Publication	New York
Publisher	IEEE
Number of pages	5
ISBN (Print)	9781424471928
DOIs	https://doi.org/10.1109/ICSET.2010.5684464
Publication status	Published - Dec 2010
Event	Sustainable Energy Technologies (ICSET) IEEE International Conference - , United Kingdom Duration: 13 Dec 2010 → …

Conference

Conference	Sustainable Energy Technologies (ICSET) IEEE International Conference
Country/Territory	United Kingdom
Period	13/12/10 → …

Keywords

artificial neural network
real time modelling
photovoltaic system
partial shading

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICSET.2010.5684464

Cite this

@inproceedings{dd65c8f9355040bd84af850ac589fa2d,

title = "Artificial neural network for real time modelling of photovoltaic system under partial shading",

abstract = "Shading caused by surrounding objects is an important issue for solar energy system design and analysis. In the special case of building integrated photovoltaic (BIPV) systems, the prediction of the partial shading is critical in order to reduce losses due to poor Maximum Power Point Tracking (MPPT). This paper will present a technique that uses Artificial Neural Network to predict the output power from a photovoltaic array in case of partial shading.",

keywords = "artificial neural network, real time modelling, photovoltaic system, partial shading",

author = "{Di Vincenzo}, {Maria Carla}",

year = "2010",

month = dec,

doi = "10.1109/ICSET.2010.5684464",

language = "English",

isbn = "9781424471928",

booktitle = "2010 IEEE International Conference on Sustainable Energy Technologies (ICSET)",

publisher = "IEEE",

note = "Sustainable Energy Technologies (ICSET) IEEE International Conference ; Conference date: 13-12-2010",

}

TY - GEN

T1 - Artificial neural network for real time modelling of photovoltaic system under partial shading

AU - Di Vincenzo, Maria Carla

PY - 2010/12

Y1 - 2010/12

N2 - Shading caused by surrounding objects is an important issue for solar energy system design and analysis. In the special case of building integrated photovoltaic (BIPV) systems, the prediction of the partial shading is critical in order to reduce losses due to poor Maximum Power Point Tracking (MPPT). This paper will present a technique that uses Artificial Neural Network to predict the output power from a photovoltaic array in case of partial shading.

AB - Shading caused by surrounding objects is an important issue for solar energy system design and analysis. In the special case of building integrated photovoltaic (BIPV) systems, the prediction of the partial shading is critical in order to reduce losses due to poor Maximum Power Point Tracking (MPPT). This paper will present a technique that uses Artificial Neural Network to predict the output power from a photovoltaic array in case of partial shading.

KW - artificial neural network

KW - real time modelling

KW - photovoltaic system

KW - partial shading

UR - http://www.scopus.com/inward/record.url?scp=79851490484&partnerID=8YFLogxK

U2 - 10.1109/ICSET.2010.5684464

DO - 10.1109/ICSET.2010.5684464

M3 - Conference contribution book

SN - 9781424471928

BT - 2010 IEEE International Conference on Sustainable Energy Technologies (ICSET)

PB - IEEE

CY - New York

T2 - Sustainable Energy Technologies (ICSET) IEEE International Conference

Y2 - 13 December 2010

ER -

Artificial neural network for real time modelling of photovoltaic system under partial shading

Abstract

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this