IET-RPG.2015.0355.pdf (648.97 kB)
Inference of missing data in photovoltaic monitoring datasets
journal contribution
posted on 2016-02-19, 10:48 authored by Elena Koumpli, Diane Palmer, Paul Rowley, Ralph GottschalgPhotovoltaic (PV) systems are frequently covered by performance guarantees, which are often based on attaining a certain performance ratio (PR). Climatic and electrical data are collected on site to verify that these guarantees are met or that the systems are working well. However, in-field data acquisition commonly suffers from data loss, sometimes for prolonged periods of time, making this assessment impossible or at the very best introducing
significant uncertainties. This study presents a method to mitigate this issue based on back-filling missing data. Typical
cases of data loss are considered and a method to infer this is presented and validated. Synthetic performance data is
generated based on interpolated environmental data and a trained empirical electrical model. A case study is subsequently used to validate the method. Accuracy of the approach is examined by creating artificial data loss in two
closely monitored PV modules. A missing month of energy readings has been replenished, reproducing PR with an average daily and monthly mean bias error of about −1 and −0.02%, respectively, for a crystalline silicon module. The PR is a key property which is required for the warranty verification, and the proposed method yields reliable results in order to achieve this.
Funding
This work has been conducted as part of the research project ‘PV2025 - Potential Costs and Benefits of Photovoltaic for UK Infrastructure and Society’ project which is funded by the RCUK’s Energy Programme (contract no: EP/K02227X/1).
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
IET Renewable Power GenerationVolume
10Issue
4Pages
434-439Citation
KOUBLI, E. ...et al., 2016. Inference of missing data in photovoltaic monitoring datasets. IET Renewable Power Generation, 10(4), pp.434-439.Publisher
© The Authors. Published by Institution of Engineering and Technology (IET)Version
- VoR (Version of Record)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/Acceptance date
2015-11-09Publication date
2016-04-01Copyright date
2016Notes
This is an Open Access Article. It is published by IET publishing under the Creative Commons Attribution 3.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/ISSN
1752-1424eISSN
1752-1424Publisher version
Language
- en