Innovating for Sustainability Driving Smart Resource Conservation in Home Appliances

The project focuses on improving energy management in home appliances using predictive modeling and data analysis. It uses historical appliance usage and environmental data to forecast energy consumption patterns. Four machine learning algorithms Linear Regression, Support Vector Regressor, Random Forest Regressor, and XGBoost Regressor are applied to train models and compare their performance. The system is designed as a web-based application using a Flask framework, allowing users to upload data and generate forecasts. The dashboard presents energy consumption insights in an organized manner, enabling effective monitoring and management of resources. Data preprocessing ensures accuracy by handling missing values and scaling features. Model evaluation uses error metrics to select the most reliable algorithm. By predicting energy usage, the system supports better planning and reduces unnecessary resource consumption. The modular design includes data upload, and prediction functionalities, ensuring ease of use and maintainability. The project demonstrates the integration of machine learning techniques with web-based tools to facilitate smart resource conservation. The methodology combines data analysis, algorithmic modeling, and interactive visualization to provide actionable insights. The results indicate that ensemble and boosting methods often outperform linear models in prediction accuracy. The project lays a foundation for further development in intelligent resource management applications.

Keywords: energy consumption, machine learning, Linear Regression, SVR, Random Forest, XGBoost, prediction, data analysis, resource conservation, Flask.