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An intelligent IoT-based home automation for optimization of electricity use

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PL
Inteligentna automatyka domowa oparta na IoT do optymalizacji zuĪycia energii elektrycznej
Języki publikacji
EN
Abstrakty
EN
The world is gearing towards renewable energy sources, due to the numerous negative repercussions of fossil fuels. There is a need to increase the efficiency of power generation, transmission, distribution, and use. The proposed work intends to decrease household electricity use and provide an intelligent home automation solution with ensembled machine learning algorithms. It also delivers organized information about the usage of each item while automating the use of electrical appliances in a home. Experimental results show that with XGBoost and Random Forest classifiers, electricity usage can be fully automated at an accuracy of 79%, thereby improving energy utilization efficiency and improving quality of life of the user.
PL
ĝwiat zmierza w kierunku odnawialnych Ĩródeá energii ze wzglĊdu na liczne negatywne reperkusje paliw kopalnych. Istnieje potrzeba zwiĊkszenia efektywnoĞci wytwarzania, przesyáu, dystrybucji i uĪytkowania energii. Proponowane prace mają na celu zmniejszenie zuĪycia energii elektrycznej w gospodarstwach domowych i zapewnienie inteligentnego rozwiązania automatyki domowej z poáączonymi algorytmami uczenia maszynowego. Dostarcza równieĪ zorganizowanych informacji na temat uĪytkowania kaĪdego elementu, jednoczeĞnie automatyzując korzystanie z urządzeĔ elektrycznych w domu. Wyniki eksperymentów pokazują, Īe dziĊki klasyfikatorom XGBoost i Random Forest zuĪycie energii elektrycznej moĪna w peáni zautomatyzowaü z dokáadnoĞcią do 79%, poprawiając w ten sposób efektywnoĞü wykorzystania energii i poprawiając jakoĞü Īycia uĪytkownika.
Rocznik
Strony
123--127
Opis fizyczny
Bibliogr. 26 poz., rys., tab.
Twórcy
  • Department of Computer Science & Engineering, Indian Institute of Information Technology Kottayam (IIITK)
  • Department of Computer Science & Engineering, Indian Institute of Information Technology Kottayam (IIITK)
  • Department of Computer Science & Engineering, Indian Institute of Information Technology Kottayam (IIITK)
  • Department of Computer Science and Media Technology, Malmö University, Sweden
  • Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria, South Afric
Bibliografia
  • [1] Juan lin and Yijuan Shen and Xin Li and Amir Hasnaoui: BRICS carbon neutrality target: Measuring the impact of electricity production from renewable energy sources and globalization, Journal of Environmental Management, 298, pp. 113460, 2021.
  • [2] International Energy Agency, Renewable electricity growth [web page],https://www.iea.org/news/renewable-electricity-growth-is-acc elerating-faster-than-ever-worldwide-supporting-the-emergence-of-t he-new-global-energy-economy. [Accessed on 18 Jun. 2022]
  • [3] bp Energy Outlook,Statistical Review of World Energy 2020 [webpage]https://www.bp.com/content/dam/bp/business-sites/en/gl obal/corporate/pdfs/energy-economics/statistical-review/bp-stats-re view-2020-full-report. [Accessed on 18 Jun. 2022.].
  • [4] International Renewable Energy Agency, Global Renewables Outlook Edition 2020 [web page] https://www.irena.org/-/media/Files/IRENA/Agency/Publication/202 0/Apr/IRENA\_Global\_Rene\\wables\_Outlook\_2020. [Accessed on 18 Jun. 2022.].
  • [5] L. Mary Gladence and V. Maria Anu and R. Rathna and E.Brumancia: Recommender system for home automation using IoT and artificial intelligence, Journal of Ambient Intelligence and Humanized Computing, Springer, 2020.
  • [6] Gray, Chrispin and Ayre, Robert and Hinton, Kerry and Camp-bell, Leith: ‘Smart’ Is Not Free: Energy Consumption of Consumer Home Automation Systems, IEEE Trans. on Consumer Electronics, 66(1), pp. 87–95, 2020.
  • [7] Zielonka, Adam and Sikora, Andrzej and Wo ғzniak, Marcin andWei, Wei and Ke, Qiao and Bai, Zongwen: Intelligent Internet of Things System for Smart Home Optimal Convection, IEEE Trans. on Industrial Informatics, 17(6), pp. 4308–4317, 2021.
  • [8] Ding, Xuefeng and Wu, Jiang: Study on Energy Consumption Optimization Scheduling for Internet of Things, IEEE Access, 7, pp. 70574–70583, 2019.
  • [9] Benjamin K. Sovacool and Dylan D. Furszyfer Del Rio: Smart home technologies in Europe: A critical review of concepts, benefits, risks and policies, Renewable and Sustainable Energy Reviews, 120, pp. 109663, 2020.
  • [10] Wazid, Mohammad and Das, Ashok Kumar and Hussain, Rasheed and Succi, Giancarlo and Rodrigues, Joel J.P.C.: Authentication in Cloud-Driven IoT-Based Big Data Environment: Survey and Outlook, Journal of System Architecture, Elsevier, 97(C), pp. 185–196, 2019.
  • [11] Dey, Shopan and Roy, Ayon and Das, Sandip: Home automation using Internet of Thing, 2016 IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 1–6, 2016.
  • [12] Lee, Chun-Te and Chen, Liang-Bi and Chu, Huan-Mei and Hsieh, Che-Jen: Design and Implementation of a Leader-Follower Smart Office Lighting Control System Based on IoT Technology, IEEE Access, 10, pp. 28066–28079, 2022.
  • [13] Al-Ali, A.R. and Zualkernan, Imran A. and Rashid, Mohammed and Gupta, Ragini and Alikarar, Mazin: A smart home energy management system using IoT and big data analytics approach, IEEE Trans. on Consumer Electronics, 63(4), pp. 426–434, 2017.
  • [14] Li, Hepeng and Wan, Zhiqiang and He, Haibo: Real-Time Residential Demand Response, IEEE Trans. on Smart Grid, 11(5), pp. 4144–4154, 2020.
  • [15] Can Li and Antonio J. Conejo and Peng Liu and Benjamin P. Omell and John D. Siirola and Ignacio E. Grossmann: Mixed-integer linear programming models and algorithms for generation and transmission expansion planning of power systems, European Journal of Operational Research, 297(3), pp. 1071–1082, 2022.
  • [16] Lingyan Cao and Yongkui Li and Jiansong Zhang and Yi Jiang and Yilong Han and Jianjun Wei: Electrical load prediction of healthcare buildings through single and ensemble learning, Energy Reports, 6, pp. 2751–2767, 2020.
  • [17] Li, Yunlong and Peng, Yiming and Zhang, Dengzheng and Mai, Yingan and Ruan, Zhengrong: XGBoost energy consumption prediction based on multi-system data HVAC, CoRR, abs/2105.09945, 2021.
  • [18] Nallathambi, Selvam and Ramasamy, Karthikeyan: Prediction of electricity consumption based on DT and RF: An application on USA country power consumption, 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering, pp. 1–7, 2017.
  • [19] A. Lahouar and J. Ben Hadj Slama: Day-ahead load forecast using random forest and expert input selection, Energy Conversion and Management, 103, pp. 1040–1051, 2015.
  • [20] Luyao Liu and Feifei Bai and Chenyu Su and Cuiping Ma and Ruifeng Yan and Hailong Li and Qie Sun and Ronald Wennersten: Forecasting the occurrence of extreme electricity prices using a multivariate logistic regression model, Energy, 247, pp. 123417, 2022
  • [21] Tiago Pinto and Isabel Praça and Zita Vale and Jose Silva: Ensemble learning for electricity consumption forecasting in office buildings, Neurocomputing, 423, pp. 747–755, 2021.
  • [22] Manogaran, Gunasekaran and Alazab, Mamoun and Saravanan, Vijayalakshmi and Rawal, Bharat S. and Shakeel, P. Mohamed and Sundarasekar, Revathi and Nagarajan, Senthil Murugan and Kadry, Seifedine Nimer and Montenegro-Marin, Carlos Enrique: Machine Learning Assisted Information Management Scheme in Service Concentrated IoT, IEEE Trans. on Industrial Informatics, 17(4), pp. 2871–2879, 2021.
  • [23] Michalski A., Starzy ғnski J., Wincenciak S.: Optimal design of the coils of the electromagnetic flow meter, IEEE Transactions on Magnetics, 34(5), pp. 2563–2566, Sep. 1998.
  • [24] Michalski A., Starzy ғnski J., Wincenciak S.: 3D Approach to Design the Excitation Coil of an Electromagnetic Flow Meter, IEEE Trans. Instrumentation and Measurement, 51(4),pp. 833–839, 2002.
  • [25] Starzy ғnski J., Szmuráo R., Michalski A.: Computer-Aided Design Tool for Electromagnetic Sensors, IEEE Instrumentation and Measurement Magazine, 12(3), pp. 28-33, Jun. 2009.
  • [26] M.I.A. Lourakis.levmar:Levenberg-marquardt nonlinear least squares algorithm in C/C++. [web page] http://www.ics.forth.gr/~lourakis/levmar/, Jul. 2004, Apr. 2009. [Accessed on 31 Jun. 2009.]
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c4713c6b-6164-419a-aa18-35945a8977b5
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