Machine Learning-Based Smart Home Data Analysis and Forecasting Method

The goal of this paper is to enable cost-effective IoT system design by removing and integrating redundant IoT sensors through correlation analysis between sensing data collected in a smart home environment. This paper presents data analysis and prediction technology that induces meaningful inference through data correlation analysis between different heterogeneous IoT sensors installed inside a smart home. Through this, we propose an intelligent service model that can be implemented based on machine learning/deep learning in a smart home environment.