Machine Learning-Based Cost-Effective Smart Home Data Analysis and Forecasting for Energy Saving

Abstract

This study aims to enable cost-effective Internet of Things (IoT) system design by removing redundant IoT sensors through the correlation analysis of sensing data collected in a smart home environment. This study also presents a data analysis and prediction technology that enables meaningful inference through correlation analysis of data from different heterogeneous IoT sensors installed inside a smart home for energy efficiency. An intelligent service model that can be implemented based on a machine learning algorithm in a smart home environment is proposed. Herein, seven types of sensor data are collected and classified into sets of input data (six environmental data) and target data (power data of HVAC). By using the six new input data, the power data can be predicted by the artificial intelligence model. The model performance was measured using RMSE, and the gradient-boosting regressor (gb) model performed the best, with an RMSE of 22.29. Also, the importance of sensor data is extracted through correlation analysis, and sensors with low importance are removed according to the importance of sensor values. This process can reduce costs by 13%, thereby providing a design guide for a cost-effective IoT system.