Decoding Smart Grid Equilibrium: Insights from Machine Learning Models

In the complex landscape of smart grid stability, this study undertakes a detailed examination of machine learning models, assessing their effectiveness, interpretability, and predictive prowess in this essential sector. With Logistic Regression, Decision Tree, Random Forest, Extra Tree and Support Vector Machine appearing as the algorithms with the most promising results, this study emphasizes the necessity of bridging the gap between complex algorithms and useful, understandable solutions for efficient smart grid management. Shapley and LIME values act as illuminating agents, revealing the importance of different features, while ROC curves articulate the classifiers’ efficacy across various thresholds with elegance and precision. The outcomes of this investigation echo the promising capabilities of machine learning in enhancing smart grid stability, albeit necessitating cautious interpretation. These methods offer insightful revelations, underscoring their potential to not only enhance predictive accuracy but also facilitate a more nuanced understanding of the intricate variables influencing grid stability. The study underscores the imperative of bridging the gap between complex machine learning algorithms and practical, interpretable solutions to navigate the intricacies of smart grid management effectively. The findings encapsulate the resonating impact of machine learning, spotlighting its latent capability to transform the smart grid stability landscape while underscoring the essentiality of nuanced interpretation. In the expansive domain of energy management, explainable AI stands as a pivotal element, heralding a new era of clarity and innovation in the intricate world of grid management.