A Comprehensive Study of Machine Learning Models and Computer Vision Techniques for Renewable Energy Forecasting

Background Of The Research

In the context of smart grids (SGs), wind is commonly acknowledged as the reliable and environmentally sustainable kind of distributed generation (DG). Therefore, it is imperative to assure the presence of these vital traits in the future of an intelligent grid. As a result, a significant body of research to improving the reliability, prediction, and detection of emerging malfunctions in specific categories of wind turbines. The study conducted by (Abedini et al. 2019) focused on the detection of wind turbine using deep learning techniques for towers. The adaptive control for the intelligent grid was analysed by (Anderson et al. 2011). (Babaie et al. 2018) introduced a novel technique for mapping drone images by utilising modern technologies. The study conducted by Bahaghighat and Motamedi (2018) focused on the examination and surveillance of wind turbine farms within developing intelligent grid systems. The identification of an advanced transmission line was investigated (Dash et al. 2006) through the utilisation of a support vector machine. In their study, Erol and Mouftah (2011) conducted an analysis of networks in the context of the future energy.