Predictive Analytics for Power Transformers and Energy Consumption with Machine Learning

The power-generating plants, along with their long transmission lines, transformers, and the network of distribution systems, result in quite a complex mesh required for the smooth delivery of electricity over long distances. Power transformers, which step up or lower voltages to facilitate effective transmission, result in failure due to wear and tear, causing disruptions in supply and huge losses in terms of economy. This project enhances grid reliability through the prediction of transformer failures and forecasting energy consumption. Historical maintenance data, operational parameters, sensor readings, and external factors in terms of weather conditions predict the health of a transformer as well as the forecast the energy demand. Machine learning models applied were Linear Regression for failure prediction, while the decision tree and Random Forest were applied for energy forecasting. The other feature which was selected on the Random Forest and the Decision Tree models shows the highest performance ever achieved and could definitely help to better improve the resilience of the grid. Even a 1% reduction in outages can save high amounts every year, making the true worth of precise predictive analytics in the power industry eminent.