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

被引：0
|
作者：
Tirmikci, Ceyda Aksoy [1 ,2 ]
Yavuz, Cenk [1 ,2 ]
Ozkurt, Cem [3 ]
Yavuz, Burcu Carkli [4 ]
机构：
[1] Sakarya Univ, Engn Fac, Elect & Elect Engn Dept, M6 Bldg,Esentepe Campus, Serdivan, Sakarya, Turkiye
[2] Sakarya Univ, Sakarya Innovat Ctr, M6 Bldg,Esentepe Campus, Serdivan, Sakarya, Turkiye
[3] Sakarya Univ Appl Sci, Technol Fac, Comp Engn Dept, Serdivan, Sakarya, Turkiye
[4] Sakarya Univ, Fac Comp & Informat Sci, Informat Syst Engn Dept, TR-54187 Serdivan, Sakarya, Turkiye
来源：
INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES | 2025年 / 20卷
关键词：
building integrated solar photovoltaic systems; PVSOL; diffuse solar radiation; CO2 emission savings; machine learning; ENERGY BUILDINGS; OPTIMIZATION; STRATEGIES; DESIGN;
D O I：
10.1093/ijlct/ctae292
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
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.
引用
收藏
页码：223 / 233
页数：11
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