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

Authors

  • Nor Zaini Zakaria
  • Hedzlin Zainuddin
  • Sulaiman Shaari
  • Ahmad Maliki Omar
  • Shahril Irwan Sulaiman

DOI:

https://doi.org/10.24191/srj.v17i1.6321

Keywords:

GCPV system, performance prediction, long-term weather data, typical meteorological year, model year climate

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 Long-Term statistical 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.

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Published

2020-02-29

How to Cite

Zakaria, N. Z., Zainuddin, H., Shaari , S. ., Omar, A. M., & Sulaiman, S. I. (2020). Grid-Connected Photovoltaic System Performance Prediction Using Long-Term Weather Data. Scientific Research Journal, 17(1), 43–57. https://doi.org/10.24191/srj.v17i1.6321