Smart Home Energy Optimization Using Big Data and Predictive Modelling

Energy efficiency is now a major concern due to the growing use of smart home technologies. In this research, a unique method for optimizing energy use through predictive modeling and big data analytics is presented. We pre-process time-series energy data and create an XGBoost regression model to predict trends of energy usage by utilizing the "Individual Household Electric Power Consumption" dataset. With a Mean Absolute Error (MAE) of 0.0178 and a Root Mean Squared Error (RMSE) of 0.0298, the results show a high degree of precision. Trend graphs and prediction plots are used to illustrate the model's performance. In order to improve energy management, we address the ramifications for smart home automation and suggest potential future interfaces with real-time systems like Home Assistant. Keywords: Smart home, energy optimization, XGBoost, machine learning, big data, predictive modeling, home automation.