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Ng, Cer Tat, Kamarudin, Muhammad Raihaan, Idris, Muhammad Idzdihar, Zainudin, Muhammad Noorazlan Shah, Muhammad, Sufry. (1404). Real-Time Prediction on Power Efficiency of Photovoltaic Thermal System with Panel Cooling Technology using Artificial Neural Network. , (), -. doi: 10.22059/jser.2026.403851.1648
Cer Tat Ng; Muhammad Raihaan Kamarudin; Muhammad Idzdihar Idris; Muhammad Noorazlan Shah Zainudin; Sufry Muhammad. "Real-Time Prediction on Power Efficiency of Photovoltaic Thermal System with Panel Cooling Technology using Artificial Neural Network". , , , 1404, -. doi: 10.22059/jser.2026.403851.1648
Ng, Cer Tat, Kamarudin, Muhammad Raihaan, Idris, Muhammad Idzdihar, Zainudin, Muhammad Noorazlan Shah, Muhammad, Sufry. (1404). 'Real-Time Prediction on Power Efficiency of Photovoltaic Thermal System with Panel Cooling Technology using Artificial Neural Network', , (), pp. -. doi: 10.22059/jser.2026.403851.1648
Ng, Cer Tat, Kamarudin, Muhammad Raihaan, Idris, Muhammad Idzdihar, Zainudin, Muhammad Noorazlan Shah, Muhammad, Sufry. Real-Time Prediction on Power Efficiency of Photovoltaic Thermal System with Panel Cooling Technology using Artificial Neural Network. , 1404; (): -. doi: 10.22059/jser.2026.403851.1648

Real-Time Prediction on Power Efficiency of Photovoltaic Thermal System with Panel Cooling Technology using Artificial Neural Network

Journal of Solar Energy Research
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 10 بهمن 1404
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22059/jser.2026.403851.1648
نویسندگان
Cer Tat Ng1؛ Muhammad Raihaan Kamarudin* 2؛ Muhammad Idzdihar Idris3؛ Muhammad Noorazlan Shah Zainudin2؛ Sufry Muhammad4
1Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer, Universiti Teknikal Malaysia Melaka, Melaka
2Fakulti Kecerdasan Buatan dan Keselamatan Siber, Universiti Teknikal Malaysia Melaka, Malaysia
3Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer, Universiti Teknikal Malaysia Melaka, Malaysia
4Fakulti Sains Komputer dan Teknologi Maklumat, Universiti Putra Malaysia, Malaysia
چکیده
Active cooling typically provides higher thermal management efficiency than passive methods. However, its continuous power consumption reduces the net energy output of photovoltaic (PV) systems. To address the limitations of traditional fixed-threshold cooling approaches, this work introduces an adaptive ANN-based hybrid cooling strategy capable of autonomously selecting the optimal cooling mode in real time. A hybrid PV cooling system integrated with Internet of Things (IoT) monitoring is developed, where a Feed Forward Neural Network Cooling System (FNNCS) is trained using real-time environmental and operational data to predict the required cooling power and intelligently choose between water- and air-based cooling. Experimental results show that the proposed FNNCS improves PV electrical performance by an average of 3.0% compared to an uncooled panel. The system achieves a maximum reduction of 14.1 °C in the backside temperature of the PV module. In addition, by dynamically adjusting cooling activation based on irradiance and temperature conditions, the FNNCS decreases cooling power consumption by 35.7% relative to a fixed cooling strategy. These findings demonstrate the effectiveness of the ANN-based hybrid cooling approach in enhancing PV performance while reducing auxiliary energy usage.
کلیدواژه‌ها
Photovoltaic؛ Cooling System؛ Feed Forward Network؛ Real Time Prediction؛ Internet of Things
آمار
تعداد مشاهده مقاله: 237





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