Grid-Connected Photovoltaic System Performance Prediction using Long-Term Weather Data

  • Nor Zaini Zakaria SPECTRA Research Group, Faculty of Applied Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Hedzlin Zainuddin SPECTRA Research Group, Faculty of Applied Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Sulaiman Shaari SPECTRA Research Group, Faculty of Applied Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Ahmad Maliki Omar GER Research Group, Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Shahril Irwan Sulaiman GER Research Group, Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

Abstract

This aim of this paper is to evaluate the accuracy of long-term weather data models for performance prediction of grid-connected photovoltaic (GCPV) systems. The analyses were done for a 6-year old metal deck roof retrofitted GCPV system located in Shah Alam, Malaysia. The monthly and annual energy yield of the actual field data for three consecutive years were compared with the predicted yield using the long-term weather data models. These models were the Typical Meteorological Year (TMY), Model Year Climate (MYC), Microclimate data, and statistical Long-Term Mean for ground station data at Subang. The findings can be a reference for photovoltaic (PV) system designers on the range of accuracy when using the weather data models for performance predictions of GCPV system in Malaysia.

Published
2020-02-29
How to Cite
ZAKARIA, Nor Zaini et al. Grid-Connected Photovoltaic System Performance Prediction using Long-Term Weather Data. Scientific Research Journal, [S.l.], v. 17, n. 1, p. 43-57, feb. 2020. ISSN 2289-649X. Available at: <https://myjms.mohe.gov.my/index.php/SRJ/article/view/6321>. Date accessed: 24 june 2024. doi: https://doi.org/10.24191/srj.v17i1.6321.
Section
Emerging materials