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Gradient boosting models for photovoltaic power estimation under partial shading conditions

Gradient boosting models for photovoltaic power estimation under partial shading conditions
Gradient boosting models for photovoltaic power estimation under partial shading conditions

The energy yield estimation of a photovoltaic (PV) system operating under partially shaded conditions is a challenging task and a very active area of research. In this paper, we attack this problem with the aid of machine learning techniques. Using data simulated by the equivalent circuit of a PV string operating under partial shading, we train and evaluate three different gradient boosted regression tree models to predict the global maximum power point (MPP). Our results show that all three approaches improve upon the state-of-the-art closed-form estimates, in terms of both average and worst-case performance. Moreover, we show that even a small number of training examples is sufficient to achieve improved global MPP estimation. The methods proposed are fast to train and deploy and allow for further improvements in performance should more computational resources be available.

Gradient boosting, Machine learning, Maximum power point (MPP), Partial shading, Photovoltaic (PV) system, Solar energy
0302-9743
13-25
Springer Verlag
Nikolaou, Nikolaos
ed61ff7c-4b80-408b-b8de-f0d38f148a76
Batzelis, Efstratios
2a85086e-e403-443c-81a6-e3b4ee16ae5e
Brown, Gavin
0f2ffd63-38e7-4a02-b965-0cfbccd495e3
Kramer, Oliver
Madnick, Stuart
Woon, Wei Lee
Aung, Zeyar
Nikolaou, Nikolaos
ed61ff7c-4b80-408b-b8de-f0d38f148a76
Batzelis, Efstratios
2a85086e-e403-443c-81a6-e3b4ee16ae5e
Brown, Gavin
0f2ffd63-38e7-4a02-b965-0cfbccd495e3
Kramer, Oliver
Madnick, Stuart
Woon, Wei Lee
Aung, Zeyar

Nikolaou, Nikolaos, Batzelis, Efstratios and Brown, Gavin (2017) Gradient boosting models for photovoltaic power estimation under partial shading conditions. Kramer, Oliver, Madnick, Stuart, Woon, Wei Lee and Aung, Zeyar (eds.) In Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy - 5th ECML PKDD Workshop, DARE 2017, Revised Selected Papers. vol. 10691 LNAI, Springer Verlag. pp. 13-25 . (doi:10.1007/978-3-319-71643-5_2).

Record type: Conference or Workshop Item (Paper)

Abstract

The energy yield estimation of a photovoltaic (PV) system operating under partially shaded conditions is a challenging task and a very active area of research. In this paper, we attack this problem with the aid of machine learning techniques. Using data simulated by the equivalent circuit of a PV string operating under partial shading, we train and evaluate three different gradient boosted regression tree models to predict the global maximum power point (MPP). Our results show that all three approaches improve upon the state-of-the-art closed-form estimates, in terms of both average and worst-case performance. Moreover, we show that even a small number of training examples is sufficient to achieve improved global MPP estimation. The methods proposed are fast to train and deploy and allow for further improvements in performance should more computational resources be available.

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More information

Published date: 2017
Additional Information: Funding Information: Acknowledgements. This project was partially supported by the EPSRC Centre for Doctoral Training [EP/I028099/1] & the EPSRC LAMBDA [EP/N035127/1] & Anyscale Apps [EP/L000725/1] project grants. N. Nikolaou acknowledges the support of the EPSRC Doctoral Prize Fellowship. E. Batzelis carried out this research at NTUA, Athens, Greece under the support of the ‘IKY Fellowships of Excellence for Postgraduate Studies in Greece-Siemens Program’. Publisher Copyright: © Springer International Publishing AG 2017. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
Venue - Dates: 5th International Workshop on Data Analytics for Renewable Energy Integration, DARE 2017, , Skopje, Macedonia, The Former Yugoslav Republic of, 2017-09-21 - 2017-09-21
Keywords: Gradient boosting, Machine learning, Maximum power point (MPP), Partial shading, Photovoltaic (PV) system, Solar energy

Identifiers

Local EPrints ID: 449664
URI: http://eprints.soton.ac.uk/id/eprint/449664
ISSN: 0302-9743
PURE UUID: aa7aa216-0e7c-4c67-878e-15f54e3bdadd

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Date deposited: 10 Jun 2021 16:31
Last modified: 10 Jun 2021 16:31

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Contributors

Author: Nikolaos Nikolaou
Author: Efstratios Batzelis
Author: Gavin Brown
Editor: Oliver Kramer
Editor: Stuart Madnick
Editor: Wei Lee Woon
Editor: Zeyar Aung

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