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.

Smart Fire Safety Management System (SFSMS) Connected with Energy Management for Sustainable Service in Smart Building Infrastructures

“…(1) physical building infrastructure management, (2) virtual-based building management, (3) data collection and management, (4) artificial intelligence (AI) and big data, and (5) platform-based integrated building management [1]. Therefore, it is important to analyze the physical and virtual elements in smart buildings, data-based AI technology, and platform-based integrated management of buildings [2]. Therefore, an important element for data-driven smart buildings is optimized building data sharing (such as energy, safety, and physical elements), which can be collected through the IoT sensors [3].…”

“…The essential elements that configure the IoT infrastructure in a building system include IoT-based sensors, 5G/6G network equipment, actuators, servers, and gateways [6]. Through these components, data collection consists of three steps: (1) IoT sensor-based data detection, (2) the transmission of the detected data to the smart gateway, and (3) the storage of the data in the server through the smart gateway. For example, consider an IoT sensor system (including temperature, humidity, motion, power, and light sensors) that is built into user A's house to collect data.…”