TY - JOUR
TI - Smart home energy optimization system
AU - Al Shehri Waleed
AU - Alghamdi Ahmed Mohammed
JN - Thermal Science
PY - 2024
VL - 28
IS - 6
SP - 5071
EP - 5085
PT - Article
AB - The proposed system outlined a scientific project aimed at developing a  machine learning-based system to optimize home energy usage and  optimization. The proposed system leverages occupancy patterns, weather  forecasts, and energy consumption data to create predictive models to  recommend energy-efficient actions to homeowners. By utilizing advanced  machine learning techniques, this research study aims to contribute  sustainable energy practices and reduce energy costs for homeowners while  minimizing environmental impact. The proposed system was developed to  analyze the data using exploratory data analysis approaches. Pre-processing  approaches are applied to prepare the data for model development. Weather  correlations are identified with the usage of energy for home appliances.  Groups are created based on the division of the date column data such as  month-wise, weekly, daily, and hourly. The algorithms for the data  forecasting used moving average, persistence algorithm, ARIMA, auto ARIMA,  LSTM univariate and LSTM multivariate. The performance of the proposed  system was evaluated by showing the graphical representation, which was very  satisfactory. The LSTM multivariate algorithm outperforms the smart home  energy optimization dataset compared to other algorithms. The outcome in the  graphical representation of the evaluation shows much satisfaction.