Machine Learning based Energy Management Model for Smart Grid

A realistic solution to the issue of energy demand regulation is the integration use of smart grid powered by consumers and renewable energy sources. the development of a successful Energy Management Model (EMM) that combines smart grids and renewable power sources is urgently needed. Unfortunately, this is a hard topic due to the uncertain circumstances and limitations. In many cases, automation (ML) techniques outperform prediction tests in modelling complicated and non-linear data. As a result, using A good substitute for the EMM is an ML technique since it makes By allowing a single train model to forecast the performance of the EMM, it is made easier. characteristics across all situations. It is still difficult to comprehend hidden connections and build confidence in very complicated Inside the stochastic consumer-based smart grid, ML models are used to create EMM. As a result, both the ML and the Gaussian Method Regression are included in the EMM in this study (GPR). To establish the Performance levels for the ML-based GPR model’s PES, PEC, and GR learning, an adapt for media related resources at its disposal (PES), feature - packed total energy (PEC), and grid revenues (GR) is developed in the first step. The second phase entails the production of a GPR PES, PEC, and GR prediction model with input from base quality attributes carrying out a task and variation of renewable power sources, stockpile, and fuel price, similar to that made available by the Simulated Annealing (GA) global optimization model for PES, PEC, and GR. The proposed approach Service-Level Agreement (SLA) of energy consumers and the grid helps both of these institutions and incorporates seasonal changes Using PES, PEC, and GR to remove barriers to prosumers’ seasonal dynamism in both consumption and production. To demonstrate the viability of the new model, the outcomes are carefully compared with those from EMMs based on traditional optimization (GA and PSO).