In particular, hybrid photovoltaic-thermal (PV-T) collectors that use a coolant to capture waste heat from the photovoltaic panels in order to deliver an additional useful thermal output are also reviewed, and it is noted that this technology has a promising potential in terms of delivering high-efficiency solar energy conversion.
To obtain high-efficiency solar photovoltaics, effective thermal management systems is of utmost. This article presents a comprehensive review that explores recent research related to thermal management solutions as applied to photovoltaic technology.
The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements. Recent advancements in artificial intelligence (AI) and the Internet of Things (IoT) have spurred innovative approaches in various domains.
Sharma et al. found that building-integrated PV systems (BIPV) with paraffin (RT42) based PCM can improve electrical efficiency by 7.7 % while decreasing the temperature by 3.8 % at an irradiance of 1000 W/m 2.
Typically, the efficiency of commercial solar PV panels ranges from about 10 % to 23 % , , . The most widely used PV panels are based on silicon (Si) cells and are categorised into three types: mono-crystalline, poly-/multi-crystalline, and amorphous.
Their results revealed that the electrical and thermal efficiencies of the combined system were 6.7 % and 33 %, respectively, compared to 7.2 % for a conventional standalone PV panel and 54 % for a conventional standalone solar-thermal collector.
In order to reach or even surpass the cost learning curve of silicon PV technology, the following key performance attributes are demanded to improve existing CPV technologies: 1) high efficiency multijunction solar cells, 2) high concentration, …