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Machine learning-assisted evaluation of PVSOL software using a real-time rooftop PV system: a case study in Kocaeli, Turkey, with a focus on diffuse solar radiation

Author

Listed:
  • Ceyda Aksoy Tırmıkçı
  • Cenk Yavuz
  • Cem Özkurt
  • Burcu Çarklı Yavuz

Abstract

Reducing energy-related CO2 emissions is vital for global climate targets, with Net Zero Energy Buildings (NZEBs) playing a key role. This study evaluates PVSOL software’s accuracy in simulating a rooftop photovoltaic (PV) system in an NZEB in Kocaeli, Turkey. A machine learning model enhanced result reliability using local weather data. The system’s first-year performance ratio was 81.9%, close to the theoretical 84.53%. The 435 600 USD investment is expected to be recovered in 11.42 years, while PVSOL predicts 14.9 years. The findings confirm PVSOL’s reliability for rooftop PV systems, emphasizing their effectiveness in CO2 reduction and energy transition efforts.

Suggested Citation

  • Ceyda Aksoy Tırmıkçı & Cenk Yavuz & Cem Özkurt & Burcu Çarklı Yavuz, 2025. "Machine learning-assisted evaluation of PVSOL software using a real-time rooftop PV system: a case study in Kocaeli, Turkey, with a focus on diffuse solar radiation," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 3590-3606.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:3590-606.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctae292
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