Predictive Analytics for Power Transformers and Energy Consumption Using Machine Learning Approaches

The growing global population, energy-hungry smart technologies, and increasing electricity consumption present numerous challenges. Nonlinear demand patterns have not yet been adequately model, even though numerous technical methods and simulation tools have been employed to determine energy consumption with high accuracy. While artificial intelligence techniques rely on historical data, conventional engineering techniques rely on dynamic equations. This research offers a novel method for projecting household power usage, which may be used to monitor and prepare for energy use more effectively. To prevent outages, power transformers, which are expensive but essential components of networks for the transmission and distribution of electricity, must be constantly monitored. An effective metric for assessing transformer status based on various factors is the health indicator (HI). While the HI approach has a wealth of documentation, there is a lack of extensive discussion of its reliability and how to handle uncertainty resulting from limited data. By using AI algorithms that account for data ambiguity, this study seeks to improve transformer health assessment accuracy. Transformer insulation is assessed using the suggested HI framework according to three criteria: paper condition, dissipated gas analysis (DGA), and oil quality. Furthermore, to enhance predictive performance, It can be provide a new hybrid algorithm called the Hybrid Insight Classifier (HIC), which combines of a contrast to performance are made an ml-based hi clone nave bayes decision tree and support vector machine a benchmark for this purpose is the scoring weighting HI approach.