An Approach based on a Robotics Operation System for the Implementation of Integrated Intelligent House Services System

Castro-Antonio, M. Kryuchcov; Carmona-Arroyo, Guadalupe; Herrera-Luna, Irving; Marı́n-Hernández, Antonio; Ríos-Figueroa, Homero Vladimir; Rechy-Ramirez, Ericka Janet

doi:10.1109/conielecomp.2019.8673166

Cited by 5 publications

(1 citation statement)

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“…The authors identified an association between technical configuration details of PV (number of inverters, number of panels, rated solar panel power, and rated inverter power) and the energy yield. In addition, Castro-Antonio et al [45] presented a Robotics Operation System (ROS) that integrates various types of services in a single smart home service system. The ROS provides services to smart homes without fully recognizing them through a collection of sensors, cameras, and by incorporating autonomous service robots that can move inside the home and interact with the residents.…”

Section: Related Workmentioning

confidence: 99%

HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving

et al. 2020

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Energy efficiency has aroused great interest in research worldwide, because energy consumption has increased in recent years, especially in the residential sector. The advances in energy conversion, along with new forms of communication, and information technologies have paved the way for what is now known as smart homes. The Internet of Things (IoT) is the convergence of various heterogeneous technologies from different application domains that are used to interconnect things through the Internet, thus allowing for the detection, monitoring, and remote control of multiple devices. Home automation systems (HAS) combined with IoT, big data technologies, and machine learning are alternatives that promise to contribute to greater energy efficiency. This work presents HEMS-IoT, a big data and machine learning-based smart home energy management system for home comfort, safety, and energy saving. We used the J48 machine learning algorithm and Weka API to learn user behaviors and energy consumption patterns and classify houses with respect to energy consumption. Likewise, we relied on RuleML and Apache Mahout to generate energy-saving recommendations based on user preferences to preserve smart home comfort and safety. To validate our system, we present a case study where we monitor a smart home to ensure comfort and safety and reduce energy consumption.

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Section: Related Workmentioning

confidence: 99%