Smart Maintenance with Regression Analysis for Efficiency Improvement in Photovoltaic Energy Systems
نویسندگان
1 Department of Alternative Energy Resources Technology Program, Hacettepe Ankara Chamber of Industry 1st Organized Industrial Zone Vocational School, Hacettepe University, Ankara, Türkiye
2 Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Kırıkkale University, Kırıkkale, Türkiye.
3 National Technical University of Athens, Athens, Greece
4 North West Regional College, Londonderry, Northern Ireland
5 Impektra IT Software, Ankara, Türkiye
6 Private Ankara Chamber of Industry Technical College Vocational and Technical Anatolian High School, Ankara, Türkiye
7 Oryx-Data Incubator EURL, Paris, France
8 Department of Alternative Energy Resources Technology Program, Hacettepe Ankara Chamber of Industry 1st Organized Industrial Zone Vocational School, Hacettepe University, Ankara, Türkiye
9 National Technical University of Athens, Athens, Greece
10 National Technical University of Athens, Athens, Greece
11 Sincan District Directorate of National Education, Ankara, Türkiye
12 Yenikent Ahmet Çiçek Vocational and Technical Anatolian High School, Ankara, Türkiye
13 Ankara Chamber of Industry 1st Organized Industrial Zone Management, Ankara, Türkiye
doi
10.22059/jser.2023.363200.1335چکیده
This research had the overarching goal of optimizing maintenance intervals and reducing the maintenance workload by enhancing accessibility for individuals lacking technical expertise in the upkeep of photovoltaic systems, with a particular focus on rooftop applications. The study achieved this objective by employing a linear regression algorithm to analyse climatic parameters such as wind speed, humidity, ambient temperature, and light intensity, collected from the installation site of a photovoltaic solar energy system. Simultaneously, the current and voltage values obtained from the system were also examined. This analysis not only facilitated the determination of power generation within the system but also enabled real-time detection of potential issues such as pollution, shadowing, bypass, and panel faults on the solar panels. Additionally, an artificial intelligence-supported interface was developed within the study, attributing any decline in power generation to specific causes and facilitating prompt intervention to rectify malfunctions, thereby ensuring more efficient system operation.
