Power grid based renewable energy analysis by photovoltaic cell machine learning architecture in wind energy hybridization

Use of solar energy systems and related green energy technology has spread around the world. When compared to conventional energy sources, solar energy is still not a frequently used energy source due to the comparatively high installation prices, low conversion rates, and battery capacity concerns. Despite the difficulties, there are numerous creative studies of new substances and new techniques for enhancing the efficiency of solar energy transformation to increase competitiveness of solar energy in market. This research proposes novel method in renewable energy analysis based on photovoltaic cell and machine learning technique for wind energy hybridization. The renewable analysis has been analysed using photovoltaic (PV) cell. Wind energy hybridization is carried out using convolutional kernel support regression vector machine. Experimental analysis has been carried out in terms of scalability, QoS (quality of service), power consumption, network efficiency, training accuracy. Financial advantages of using new cooling methods for photovoltaic panels are also assessed through a cost analysis.