Demand side energy management for smart homes using a novel learning technique-economic analysis aspects

In this study, a new distributed pattern of demand side energy management in residential smart grids regarding Improved Sparse Bayesian Learning (ISBL) method along different types of domestic electrical appliances have been presented. Considering the overall expense of electricity usage, the restrictions of supply quantity is sustained through distribution set-ups and the obtained power demands for individual appliances, the optimal energy management (OEM), for the demand side users, such as households, became an elaborated optimization issue in relation to the coupled objective performance across spatially and temporally of doubled constraints. Usually, in distributed model, figuring out of this kind of issue is problematic. Hence, in this study, we had changed it into ISBL method, and developed a distributed algorithm in order to obtain an optimal quantity of the unified OEM across the procedure of implementation. The presented scheme does not need any personal information about individual operators, meanwhile each of optimality and convergence are achieved. Additionally, this article demonstrated that the presented scheme has high robustness in unreliable communications. In addition, the suggested scheme is demonstrated and confirmed through simulations.