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Title: Inference of missing PV monitoring data using neural networks
Authors: Koubli, Eleni
Palmer, Diane
Betts, Thomas R.
Rowley, Paul
Gottschalg, Ralph
Keywords: Missing data
Monitoring data
Neural networks
Solar radiation data
Photovoltaic (PV) systems
Issue Date: 2016
Publisher: © IEEE
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.
Abstract: © 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.
Description: 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.
Sponsor: 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.
Version: Accepted version
DOI: 10.1109/PVSC.2016.7750305
URI: https://dspace.lboro.ac.uk/2134/27501
Publisher Link: https://doi.org/10.1109/PVSC.2016.7750305
ISBN: 9781509027248
ISSN: 0160-8371
Appears in Collections:Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)

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