Error Assessments of Power Generation Using Logistic Regression in Smart Grid Connected to Natural Energy Resources

Natural energy resources (NERs) are essential to meeting future energy demands, but they are susceptible to faults caused by defects in components and dynamic environmental conditions. This paper proposes the Error Assessments of Power Generations using Logistic Regression (EAPGLR) system for fault detection and efficient management of NER-connected smart grids. This statistical method is used in binary classification problems. The important aspect is to estimate the potential sources of error in the overall process. Logistic regression always estimate a linear relationship between the independent variables and the log-odds of the dependent variable in the system. In the power generation the regression algorithm overgeneralizes the intricate interactions among diverse natural energy resources and the performance of the grid. The interdependencies and correlations in various components result in the occurrence of errors in the system. The important consideration is the assumption of a constant relationship over time. The dynamic nature is obtained through the integration of renewable energy systems which create temporal dependencies in energy production patterns. EAPGLR achieves higher AUC values for accuracy levels, demonstrating its effectiveness in fault detection. EAPGLR can improve the reliability and efficiency of smart grids powered by NERs, reduce maintenance costs, and extend the lifespan of NER assets.