Abstract
References
- Acikgoz H. (2022).A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting. Appl Energy,305. 117912..
- Ali, M.U., Khan, H.F., Masud M., Kallu K.D., & Zafar, A. (2020) A machine learning framework to identify the hotspot in photovoltaic module using infrared thermography. Solar Energy, 208,643–651.
- Amidi, A., Amidi, S., Vlachakis, D., Paragios, N., & Zacharaki, EI. (2016) A machine learning methodology for enzyme functional classification combining structural and protein sequence descriptors. Lecture Notes in Computer Science, 9656, 728–738.
Abstract
Solar energy production is provided by thermal and photovoltaic (PV) systems. Among them, PVs are considered one of the most important power generation systems that produce safe and sustainable energy. Depending on the outdoor conditions, PV systems get dirty over time due to dust, rain and environmental factors. Above the PV system When polluted, it causes a significant decrease in their efficiency. Especially in large photovoltaic power plants, it is seen that the panels are not polluted equally. But the cleaning robots system When it starts to clean, it cleans all the panels. Cleaning all clean and dirty panels causes energy consumption, material life and spare parts waste. In the system we have planned, it is planned to clean the panels by detecting the dirty panels with the camera and directing the cleaning robot only to the contaminated areas. Since only the contaminated areas will be cleaned, the energy production efficiency of the panels will increase and the cleaning time will be shortened.
References
- Acikgoz H. (2022).A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting. Appl Energy,305. 117912..
- Ali, M.U., Khan, H.F., Masud M., Kallu K.D., & Zafar, A. (2020) A machine learning framework to identify the hotspot in photovoltaic module using infrared thermography. Solar Energy, 208,643–651.
- Amidi, A., Amidi, S., Vlachakis, D., Paragios, N., & Zacharaki, EI. (2016) A machine learning methodology for enzyme functional classification combining structural and protein sequence descriptors. Lecture Notes in Computer Science, 9656, 728–738.
Details
| Primary Language | English |
|---|---|
| Subjects | Photovoltaic Power Systems |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | August 27, 2023 |
| Publication Date | September 1, 2023 |
| Published in Issue | Year 2023Volume: 22 |
