Journal of Artificial Intelligence and Metaheuristics

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https://doi.org/10.54216/JAIM

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Volume 6 , Issue 2 , PP: 08-15, 2023 | Cite this article as | XML | Html | PDF | Full Length Article

An Evaluation of ARIMA and Persistence Models in IoT-Driven Smart Homes

Ahmed Abdelmgeed 1 * , Ahmed Mohamed Zaki 2 , Marwa Adel Soliman 3

  • 1 Department of Communications & Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, Egypt - (ahmedabdelmageed284@gmail.com)
  • 2 Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA - (azaki@jcsis.org)
  • 3 Department of Architecture, Delta Higher Institute of Engineering and Technology, Mansoura, Egypt - (marwa_elfiky@mans.edu.eg)
  • Doi: https://doi.org/10.54216/JAIM.060201

    Received: May 13, 2023 Revised: August 02, 2023 Accepted: December 03, 2023
    Abstract

    Commencing with the transformative fusion of Smart Home and Internet of Things (IoT) technologies, this study scrutinizes the efficacy of predictive modeling approaches, specifically the autoregressive integrated moving average (ARIMA) and persistence algorithms. The primary focus lies in their potential for forecasting and optimizing energy consumption dynamics within the intricate framework of smart homes. The investigation reveals a nuanced comparison between the proposed ARIMA and conventional Persistence models. Smart Home, emblematic of innovative living, integrates seamlessly with IoT, promising an intelligent and interconnected domestic ecosystem. To enhance energy efficiency, this study explores the ARIMA model's capabilities alongside the persistence algorithm. Notably, the proposed ARIMA model showcases exceptional prowess in forecasting, substantiated by a significantly lower  compared to the Persistence model. The ARIMA model, with an Root Mean Square Error value of 0.03378, outshines the Persistence model with a higher Root Mean Square Error value of 0.158 when evaluated on the test dataset. This substantial reduction in  emphasizes the superior performance of the ARIMA model, making it a compelling choice for time series forecasting tasks. Beyond quantitative metrics, the precision of the ARIMA model holds transformative potential, promising cost-effective energy consumption, proactive maintenance, and an elevated quality of life within smart homes. This research establishes a robust foundation for integrating advanced predictive modeling, particularly the ARIMA model, to enhance the efficiency, sustainability, and inhabitant satisfaction of smart homes in the era of IoT.

    Keywords :

    Smart Home , ARIMA model , Time Series, IOT ,   , Persistence algorithm , ARIMA model , Modern architecture.

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    Cite This Article As :
    Abdelmgeed, Ahmed. , Mohamed, Ahmed. , Adel, Marwa. An Evaluation of ARIMA and Persistence Models in IoT-Driven Smart Homes. Journal of Artificial Intelligence and Metaheuristics, vol. , no. , 2023, pp. 08-15. DOI: https://doi.org/10.54216/JAIM.060201
    Abdelmgeed, A. Mohamed, A. Adel, M. (2023). An Evaluation of ARIMA and Persistence Models in IoT-Driven Smart Homes. Journal of Artificial Intelligence and Metaheuristics, (), 08-15. DOI: https://doi.org/10.54216/JAIM.060201
    Abdelmgeed, Ahmed. Mohamed, Ahmed. Adel, Marwa. An Evaluation of ARIMA and Persistence Models in IoT-Driven Smart Homes. Journal of Artificial Intelligence and Metaheuristics , no. (2023): 08-15. DOI: https://doi.org/10.54216/JAIM.060201
    Abdelmgeed, A. , Mohamed, A. , Adel, M. (2023) . An Evaluation of ARIMA and Persistence Models in IoT-Driven Smart Homes. Journal of Artificial Intelligence and Metaheuristics , () , 08-15 . DOI: https://doi.org/10.54216/JAIM.060201
    Abdelmgeed A. , Mohamed A. , Adel M. [2023]. An Evaluation of ARIMA and Persistence Models in IoT-Driven Smart Homes. Journal of Artificial Intelligence and Metaheuristics. (): 08-15. DOI: https://doi.org/10.54216/JAIM.060201
    Abdelmgeed, A. Mohamed, A. Adel, M. "An Evaluation of ARIMA and Persistence Models in IoT-Driven Smart Homes," Journal of Artificial Intelligence and Metaheuristics, vol. , no. , pp. 08-15, 2023. DOI: https://doi.org/10.54216/JAIM.060201