Multi-criteria decision-making approaches to resource optimization in renewable energy systems

Energy Management with Sustainability Creates, an urgent Need In the management of energy sustainably creates an urgent need for the development of new, flexible approaches that take into account the environment, economy, and social equity. Conventional approaches are too rigid to adopt changes both in technological and environmental spheres. This paper will introduce the resource allocation method that uses sustainable energy, which will inspire four new methodologies, for instance, DRL-DRA. These utilize deep and reinforcement learning techniques with Multi-Criteria Decision Making concerning energy distribution in order to minimize carbon emissions, along with the maximization of exploitation of renewable energy sources. PA-EDF provides accurate computation for future energy demands and affords planners an opportunity to regain lost ground. Third one is CA-TIS. GRL-SRA uses geography to inform the setting of energy user policy sets similar to others using the MCDM to locate energy infrastructures with the help of environmental consideration, space, etc. The methods together have been shown to impressively cut carbon emissions by 28 %, boost renewable energy use by 35 %, and improve energy equity by 22 %, far exceeding the benchmarks of traditional methods. In showing how the methods work together in process, this study illustrates how machine learning can enhance energy management for social situations.