On the Interdependence and Importance of Meteorological Variables for Photovoltaic Output Power Estimation

T. AlSkaif, S. Dev, L. Visser, M. Hossari, W. van Sark

Research output: Contribution to conferencePaperAcademic

Abstract

While the large-scale deployment of Photovoltaic (PV) systems plays an important role in limiting global warming, the variability of PV output power poses challenges in grid management. Typically, the PV output power is dependent on various meteorological parameters at the PV site. In this paper, we analyse the interdependence of different meteorological variables and show their importance for PV output power estimation. Using Principal Component Analysis (PCA), we identify the primary meteorological variables for PV output power estimation. The numerical evaluation is performed using 3 years long of 9 meteorological variables data and PV output power data of 10 distinct rooftop PV systems, located in the city of Utrecht, the Netherlands. Simulation results show the interdependence between the meteorological variables and demonstrate that relative humidity, visibility, temperature and cloud cover are the most important variables for estimating PV output power in Utrecht.
Original languageEnglish
Pages2117-2120
Number of pages4
DOIs
Publication statusPublished - 2019
Event46th IEEE Photovoltaic Specialists Conference (PVSC 46) - Chicago, Il, United States
Duration: 16 Jun 201921 Jun 2019

Conference

Conference46th IEEE Photovoltaic Specialists Conference (PVSC 46)
Country/TerritoryUnited States
CityChicago, Il
Period16/06/1921/06/19

Keywords

  • building integrated photovoltaics
  • global warming
  • meteorology
  • power grids
  • principal component analysis
  • meteorological parameters
  • PV output power estimation
  • primary meteorological variables
  • photovoltaic output power estimation
  • distinct rooftop PV systems
  • grid management
  • numerical evaluation
  • Utrecht
  • Netherlands
  • Photovoltaic
  • Meteorological variables
  • Cross-correlation
  • Principal component analysis
  • Regression

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