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Inference of missing PV monitoring data using neural networks

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conference contribution
posted on 17.11.2017 by Elena Koumpli, Diane Palmer, Tom Betts, Paul Rowley, Ralph Gottschalg
© 2016 IEEE.Complete photovoltaic monitoring data are required in order to evaluate PV system performance and to ensure confidence in project financing. Monitoring sub-system failures are common occurrences, reducing data availability in meteorological and electrical datasets. A reliable backfilling method can be applied in order to mitigate the impact of long monitoring gaps on system state and performance assessment. This paper introduces a method of inferring in-plane irradiation from remotely obtained global horizontal irradiation, by means of a neural network approach. Generation output is then calculated utilizing a simple electrical model with fitted coefficients. The proposed method is applied to a UK case study for which the mean absolute error in monthly system output was reduced significantly, to as low as 0.9%. This yielded more accurate results in backfilling the missing datasets when compared to standard approaches. The impact of missing data on monthly performance ratio is also investigated. Using backfilling to synthesize lost data increases performance ratio prediction accuracy significantly when compared to simply omitting such periods from the calculation.

Funding

This work has been conducted as part of the research project ‘PV2025 - Potential Costs and Benefits of Photovoltaic for UK Infrastructure and Society’, funded by the RCUK Energy Programme (contract no: EP/K02227X/1). The authors would like to acknowledge the additional support of the European Metrology Research Programme (EMRP) Project ENG55 PhotoClass.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

43rd IEEE Photovoltaic Specialists Conference Conference Record of the IEEE Photovoltaic Specialists Conference

Volume

2016-November

Pages

3436 - 3440

Citation

KOUBLI, E. ...et al., 2016. Inference of missing PV monitoring data using neural networks. Presented at the 43rd IEEE Photovoltaic Specialists Conference (PVSC 16), Portland, Oregon, USA, 5-10 June, pp. 3436-3440.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Acceptance date

05/05/2016

Publication date

2016

Notes

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.

ISBN

9781509027248

ISSN

0160-8371

Language

en

Location

Portland, Oregon, USA

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