| dc.contributor.author | Machorro-Cano, Isaac | |
| dc.contributor.author | Alor-Hernández, Giner | |
| dc.contributor.author | Paredes Valverde, Mario Andres | |
| dc.contributor.author | Rodríguez Mazahua, Lisbeth | |
| dc.contributor.author | Sánchez-Cervantes, José Luis | |
| dc.contributor.author | Olmedo-Aguirre, José Oscar | |
| dc.date.accessioned | 2021-07-07T15:06:08Z | |
| dc.date.available | 2021-07-07T15:06:08Z | |
| dc.date.issued | 2020-03-02 | |
| dc.identifier.citation | Machorro-Cano, Isaac, Giner Alor-Hernández, Mario A. Paredes-Valverde, Lisbeth Rodríguez-Mazahua, José L. Sánchez-Cervantes, and José O. Olmedo-Aguirre 2020. "HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving" Energies 13, no. 5: 1097. https://doi.org/10.3390/en13051097 | es |
| dc.identifier.issn | 1996-1073 | |
| dc.identifier.other | https://doi.org/10.3390/en13051097 | |
| dc.identifier.uri | http://repositorios.orizaba.tecnm.mx:8080/xmlui/handle/123456789/491 | |
| dc.description | 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 | es |
| dc.description.abstract | 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 | es |
| dc.description.sponsorship | Funding
This research was funded by Mexico’s National Council of Science and Technology (CONACYT) through project 52–2016: "Application of Big Data and Semantic Web techniques to Develop Intelligent Systems," a postdoctoral grant, and a doctoral grant.
Acknowledgments
This work was supported by Mexico’s National Technological Institute (TecNM) and sponsored by both Mexico’s National Council of Science and Technology (CONACYT) and the Secretariat of Public Education (SEP) through the PRODEP project (Programa para el Desarrollo Profesional Docente).
Conflicts of Interest | es |
| dc.language.iso | en_US | es |
| dc.publisher | MDPI Publishing | es |
| dc.relation.ispartofseries | Energies; | |
| dc.subject | domotic | es |
| dc.subject | energy saving | es |
| dc.subject | Internet of Things | es |
| dc.subject | machine learning | es |
| dc.title | HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving | es |
| dc.type | Article | es |
