Advanced Machine Learning-Based High Demand Forecasting of Household Energy Consumption for Enhancing Grid Operations

This research examines methods for predicting household energy usage to assist in the management of peak demand and the maintenance of grid stability. The focus is on forecasting when energy consumption surpasses certain critical levels and for what duration, allowing for proactive energy management. The study looks at the impact of various data aggregation techniques on prediction accuracy and explores approaches to refine altered consumption patterns for better forecasting. By evaluating different forecasting models and their effectiveness, the work aims to enhance energy management, promote automation in grid operations, and strengthen data-driven decision-making for a more resilient and efficient power distribution system.