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Towards efficient human–machine interaction for home energy management with seasonal scheduling using deep fuzzy neural optimizer

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Abstract

Maintaining the records of domestic consumers’ electricity consumption patterns is very complex task for the utilities, especially for extracting the meaningful information to maintain their demand and supply. Due to the increase in population, large amount of valuable data from the domestic sector is extracted by the smart meters and it becomes a vulnerable issue to tackle this information in recent era. In this work, we have proposed the fuzzy deep neural optimizer to optimize the cost and power demand of the stochastic behavior of the domestic consumers. For optimization process, this optimizer considers three control parameters: energy consumption, time of the day, and price and two performance parameters: cost and peak reduction. The dataset used for this optimization process is of two seasons: summer and winter season and it is obtained from Pecan Street Incorporation site. Takagi Sugeno fuzzy inference system is applied for the computation of the rules, which are formulated using the Membership Functions (MFs) of the aforementioned parameters. The nature of the MFs is chosen as Gaussian MFs to continuously monitoring the consumers’ behaviors at different time intervals. Simulations are performed to show the robustness of the proposed optimizer in terms of energy efficiency and cost optimization up to 8 kWh and 1$ for the summer season and 12.5 kWh and 4$ for winter season. The proposed optimizer outperforms the previous scheme with remarkable results and highly recommended for the future systems where consumers are growing tremendously.

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Acknowledgements

This Research is funded by Research Supporting Project Number (RSPD2023R553, King Saud University, Riyadh, Saudi Arabia.

Funding

This work was supported by King Saud University, Riyadh, Saudi Arabia, through Researchers Supporting Project No. (RSPD2023Rxxx).

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Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad, Islamabad, 44000, Pakistan

    Sakeena Javaid & Nadeem Javaid

  2. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O.Box 51178, Riyadh, 11543, Kingdom of Saudi Arabia

    Musaed Alhussein & Khursheed Aurangzeb

  3. Department of Computing, National University of Sciences and Technology Islamabad, Islamabad, 44000, Pakistan

    Sohail Iqbal

  4. Department of AI and Software, Gachon University, Seongnam-si, 13120, South Korea

    Muhammad Shahid Anwar

Authors
  1. Sakeena Javaid
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  2. Nadeem Javaid
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  3. Musaed Alhussein
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  4. Khursheed Aurangzeb
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  5. Sohail Iqbal
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  6. Muhammad Shahid Anwar
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Contributions

Writing–original draft preparation, SJ and NJ; writing–review and editing, NJ, SI, and KA; visualization, SI and MA; supervision, NJ and MSA; project administration, MA and KA; simulations, SJ, SI, and MSA; funding acquisition and project management, KA and MA. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Nadeem Javaid.

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Javaid, S., Javaid, N., Alhussein, M. et al. Towards efficient human–machine interaction for home energy management with seasonal scheduling using deep fuzzy neural optimizer. Cogn Tech Work 25, 291–304 (2023). https://doi.org/10.1007/s10111-023-00728-4

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  • DOI: https://doi.org/10.1007/s10111-023-00728-4

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