Feasibility study on machine‐learning‐based hybrid renewable energy applications for engineering education
Shilaja Chandrasekaran
Abstract
Abstract In addition to the conventional natural resources such as petroleum extracts, natural gas and charcoal, and particularly with the current worldwide condition of economy of the energy sector, renewable energy sources are increasingly gaining attention. On the basis of recent researches, experts and environmentalists suggest that the renewable energy sources contribute for the major energy consumption. Rapid development of the renewable energy and energy‐efficient technologies results in significant energy security and economic benefits, which lead to reduction in capital investment for electricity systems. Therefore, through power system planning, we can augment the quality and efficiency of the power supply. The challenges in meeting the power requirements can be addressed by machine learning (ML) technique, an interdisciplinary field that allows using the statistical techniques to solve the energy‐related problems using pattern recognition, artificial neural network, and fuzzy and hybrid combinations. The recent innovations in web‐based and mobile technologies led to the application of ML in energy sector.