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

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.