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Statistical modeling, parameter estimation and measurement planning for PV degradation

  • Dazhi Yang*
  • , Licheng Liu
  • , Carlos David Rodríguez-Gallegos
  • , Zhen Ye
  • , Li Hong Idris Lim
  • , Omid Geramifard
  • *Corresponding author for this work
  • Agency for Science, Technology and Research, Singapore
  • Saferay Pte. Ltd.
  • National University of Singapore
  • Elkem ASA
  • University of Glasgow

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Photovoltaics (PV) degradation is a key consideration during PV performance evaluation. Accurately predicting power delivery over the course of lifetime of PV is vital to manufacturers and system owners. With many systems exceeding 20 years of operation worldwide, degradation rates have been reported abundantly in the recent years. PV degradation is a complex function of a variety of factors, including but not limited to climate, manufacturer, technology and installation skill. As a result, it is difficult to determine degradation rate by analytical modeling; it has to be measured. As one set of degradationmeasurements based on a single sample cannot represent the population nor be used to estimate the true degradation of a particular PV technology, repeated measures through multiple samples are essential. In this chapter, linear mixed effects model (LMM) is introduced to analyze longitudinal degradation data. The framework herein introduced aims to address three issues: 1) how to model the difference in degradation observed in PV modules/systems of a same technology that are installed at a shared location; 2) how to estimate the degradation rate and quantiles based on the data, and 3) how to effectively and efficiently plan degradation measurements.

Original languageEnglish
Title of host publicationSolar Energy and Solar Panels
Subtitle of host publicationSystems, Performance and Recent Developments
PublisherNova Science Publishers, Inc.
Pages123-150
Number of pages28
ISBN (Electronic)9781536104080
ISBN (Print)9781536103809
StatePublished - 1 Jan 2017
Externally publishedYes

UN SDGs

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

  1. SDG 13 - Climate Action
    SDG 13 Climate Action

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