Weather-Adaptive Power Factor Management: A Machine Learning Approach

The low power factor condition is influenced by various aspects one of the main being weather and load variations, a clever and sophisticated solution is necessary. With suboptimal power factors leading to penalties as well as due to the weather changes, the consumption will also vary, a single policy won't solve all problems. We hope to study the effect of weather conditions on power factors and develop an auto-corrective system. Through supervised learning, using Logistic Regression, Support Vector Machine (SVM), Decision Trees, and Random Forest Models, we have attained a high precision of 93 %. Cross-validation further strengthens the reliability by more than 99.7 %. This solution adjusts capacitors in real time based on weather data, which optimizes the power factor as precisely as possible. This study paves the way for an intelligent method to resolve power factor problems, improving efficiency and avoiding financial penalties.