Support Vector Regression Approach to Optimal Power Flow Analysis in Smart Grids with IoT

For optimal power flow (OPF) analysis, incorporating Internet of Things (IoT) technologies into smart grids poses additional difficulties. To improve the precision and effectiveness of OPF in smart grid settings, this research proposes using Support Vector Regression (SVR). The SVR model successfully captures the nonlinear interactions between several grid features, including voltage levels, generating capabilities, and load demands, using data gathered from IoT devices. Simulations on a benchmark power system demonstrate the proposed approach, showing that it outperforms conventional optimization methods in terms of computing time and accuracy of the solution. The SVR technique is especially well-suited for dynamic smart grid applications as it also exhibits strong performance in situations with variable degrees of power production and consumption uncertainty. The findings show that considering the complexity of distributed energy resources and real-time data analytics, SVR implementation for OPF analysis may greatly increase grid dependability and operational efficiency.