Energy Saving by Using Internet of Things Paradigm and Machine Learning

Nowadays, energy consumption is acquiring growing attention for the economic and environmental implications in our society due to the growing number of electronic home devices. From this perspective, the Internet of Things (IoT) and Machine Learning have emerged as technologies that allow monitoring and controlling devices installed in houses to detect behavioral patterns that identify feasible scenarios of energy saving. For this reason, intelligent configuration approaches for home automation are of utmost importance. This paper proposes a mobile application (called IntelihOgarT) that optimizes energy consumption through Machine Learning and IoT, while improving, at the same time, comfort at home. The proposed application makes use of Machine Learning algorithm C4.5, which automatically takes decisions based on attributes of a training data set. Furthermore, the case study presented validates the effectiveness of the mobile application, where efficient use of energy at home is a primary concern.

[1]  Junzo Watada,et al.  Metaheuristic Techniques in Enhancing the Efficiency and Performance of Thermo-Electric Cooling Devices , 2017 .

[2]  Ivan Zelinka,et al.  A novel approach on evolutionary dynamics analysis - A progress report , 2017, J. Comput. Sci..

[3]  Diego P. Chacón-Troya,et al.  Domotic application for the monitoring and control of residential electrical loads , 2017, 2017 IEEE 37th Central America and Panama Convention (CONCAPAN XXXVII).

[4]  Emanuele Frontoni,et al.  Design of an interoperable framework with domotic sensors network integration , 2017, 2017 IEEE 7th International Conference on Consumer Electronics - Berlin (ICCE-Berlin).

[5]  Thillainathan Logenthiran,et al.  Implemented IoT-Based Self-Learning Home Management System (SHMS) for Singapore , 2018, IEEE Internet of Things Journal.

[6]  Gautam Srivastava,et al.  A Secure Publish/Subscribe Protocol for Internet of Things , 2019, IACR Cryptol. ePrint Arch..

[7]  Syed Sibte Raza Abidi,et al.  A Predictive Model for Personalized Therapeutic Interventions in Non-small Cell Lung Cancer , 2016, IEEE Journal of Biomedical and Health Informatics.

[8]  Kanae Matsui,et al.  An Information Provision System as a Function of HEMS to Promote Energy Conservation and Maintain Indoor Comfort , 2017 .

[9]  Pandian Vasant,et al.  Advances in Metaheuristics: Applications in Engineering Systems , 2016 .

[10]  Jens Teubler,et al.  The Multiple Benefits of the 2030 EU Energy Efficiency Potential , 2019, Energies.

[11]  Oscar Gabriel Fuentes Lanfor,et al.  Implementación de un sistema de seguridad independiente y automatización de una residencia por medio del internet de las cosas , 2017, 2017 IEEE Central America and Panama Student Conference (CONESCAPAN).

[12]  Antonio Piccinno,et al.  EnergyAware: a non-intrusive load monitoring system to improve the domestic energy consumption awareness , 2019, EnSEmble@ESEC/SIGSOFT FSE.

[13]  Rachid Maouedj,et al.  Contribution to the modeling and simulation of multiagent systems for energy saving in the habitat , 2017, 2017 International Conference on Mathematics and Information Technology (ICMIT).

[14]  Iakovos S. Venieris,et al.  Collective domotic intelligence through dynamic injection of semantic rules , 2015, 2015 IEEE International Conference on Communications (ICC).

[15]  David Hemmendinger,et al.  Encyclopedia of computer science (4th ed.) , 2000 .

[16]  Anna Fensel,et al.  Contributing to appliances' energy efficiency with Internet of Things, smart data and user engagement , 2017, Future Gener. Comput. Syst..

[17]  Ricardo Jardim-Gonçalves,et al.  A multi-criteria decision model for the selection of a more suitable Internet-of-Things device , 2017, 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC).

[18]  Marco Aiello,et al.  Coordinating the web of services for a smart home , 2013, TWEB.

[19]  Nazar Zaki,et al.  Detection of Masses in Digital Mammogram Using Second Order Statistics and Artificial Neural Network , 2011 .

[20]  Leandro A. Villas,et al.  Energy-efficient smart home systems: Infrastructure and decision-making process , 2019, Internet Things.

[21]  Khaled Grayaa,et al.  Novel home energy management system using wireless communication technologies for carbon emission reduction within a smart grid , 2016 .

[22]  Pandian Vasant,et al.  Nature-inspired meta-heuristics approaches for charging plug-in hybrid electric vehicle , 2020, Wirel. Networks.

[23]  Antonio Marín-Hernández,et al.  An Approach based on a Robotics Operation System for the Implementation of Integrated Intelligent House Services System , 2019, 2019 International Conference on Electronics, Communications and Computers (CONIELECOMP).

[24]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[25]  Jo Ueyama,et al.  A Low-Cost Smart Home Automation to Enhance Decision-Making based on Fog Computing and Computational Intelligence , 2018, IEEE Latin America Transactions.

[26]  Bandar Aldawsari,et al.  An energy-aware service composition algorithm for multiple cloud-based IoT applications , 2017, J. Netw. Comput. Appl..

[27]  Danaipong Chetchotsak,et al.  Improve discrimination power of serum markers for diagnosis of cholangiocarcinoma using data mining-based approach. , 2015, Clinical biochemistry.

[28]  Ludovic Noirie,et al.  IoT Composer: Composition and Deployment of IoT Applications , 2019, 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion).