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Journal of Artificial Intelligence and Metaheuristics

Volume 8 , Issue 2 , PP: 10-18, 2024 | Cite this article as | XML | Html | PDF | Full Length Article

Smart Home Energy Management through ARIMA Model Forecasting: Leveraging Weather Data for Improved Efficiency

El-Sayed M. El-Kenawy 1 , Marwa M. Eid 2 , Abdelhameed Ibrahim 3 , Osama Alabedallat 4

  • 1 School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain; Jadara University Research Center, Jadara University, Jordan; Applied Science Research Center, Applied Science Private University, Amman, Jordan - (skenawy@ieee.org)
  • 2 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt - (mmm@ieee.org)
  • 3 School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain - (abdelhameed.fawzy@polytechnic.bh)
  • 4 School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain - (osama.alabedallat@polytechnic.bh)

    • Doi: https://doi.org/10.54216/JAIM.080202

    Received: March 26, 2024 Revised: May 28, 2024 Accepted: November 03 2024
    Abstract

    This study pursues machine learning models for the task of smart homes' energy management with the use of a dataset that combines smart meter readings and weather conditions at the same time. The assessment of the Baseline Qualification and ARIMA models is done using various criteria, such as MSE, RMSE, and others. Most telling, the best performance is shown by ARIMA, which gets the lowest MSE score, 0.0693, in this instance. They show that such a model is optimal in forecasting energy consumption dynamics, and while they could be better, weather information helps improve the accuracy of the forecasts. The conduct helps uncover priceless information, allowing for the development of new smart home operating systems with a prospect of energy efficiency enhancement as well as a sustainable environment.

    Keywords :

    Smart homes , Home energy management , ARIMA models , Smart home operating systems

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    Cite This Article As :
    M., El-Sayed. , M., Marwa. , Ibrahim, Abdelhameed. , Alabedallat, Osama. Smart Home Energy Management through ARIMA Model Forecasting: Leveraging Weather Data for Improved Efficiency. Journal of Artificial Intelligence and Metaheuristics, vol. , no. , 2024, pp. 10-18. DOI: https://doi.org/10.54216/JAIM.080202
    M., E. M., M. Ibrahim, A. Alabedallat, O. (2024). Smart Home Energy Management through ARIMA Model Forecasting: Leveraging Weather Data for Improved Efficiency. Journal of Artificial Intelligence and Metaheuristics, (), 10-18. DOI: https://doi.org/10.54216/JAIM.080202
    M., El-Sayed. M., Marwa. Ibrahim, Abdelhameed. Alabedallat, Osama. Smart Home Energy Management through ARIMA Model Forecasting: Leveraging Weather Data for Improved Efficiency. Journal of Artificial Intelligence and Metaheuristics , no. (2024): 10-18. DOI: https://doi.org/10.54216/JAIM.080202
    M., E. , M., M. , Ibrahim, A. , Alabedallat, O. (2024) . Smart Home Energy Management through ARIMA Model Forecasting: Leveraging Weather Data for Improved Efficiency. Journal of Artificial Intelligence and Metaheuristics , () , 10-18 . DOI: https://doi.org/10.54216/JAIM.080202
    M. E. , M. M. , Ibrahim A. , Alabedallat O. [2024]. Smart Home Energy Management through ARIMA Model Forecasting: Leveraging Weather Data for Improved Efficiency. Journal of Artificial Intelligence and Metaheuristics. (): 10-18. DOI: https://doi.org/10.54216/JAIM.080202
    M., E. M., M. Ibrahim, A. Alabedallat, O. "Smart Home Energy Management through ARIMA Model Forecasting: Leveraging Weather Data for Improved Efficiency," Journal of Artificial Intelligence and Metaheuristics, vol. , no. , pp. 10-18, 2024. DOI: https://doi.org/10.54216/JAIM.080202

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