Household Power Consumption Analysis using Machine Learning

To understand the complex relationships between the power consumption of various household appliances and the overall power usage, this essay delves into the field of machine learning. The principal aim is to develop prognostic models that can estimate the overall active power consumption by analyzing the energy consumption of designated household spaces, such as the kitchen, laundry room, air conditioning unit, and electric water heater. Through meticulous analysis and implementation of machine learning techniques, insight into the fundamental connections that underpin patterns of energy consumption is shed. The implications of the findings extend beyond mere predictive accuracy, offering invaluable insights for optimizing energy usage and informing future power management strategies. This study emphasizes how important it is to apply machine learning to interpret trends in household power consumption to make informed decisions and promote sustainable power use. Momentarily, household power consumption monitoring utilizing the XG Boost algorithm will make use of sophisticated data analytics to maximize energy use, improve efficiency, and give customers individualized advice on sustainable living habits.