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Home Energy Management System Based on Genetic Algorithm for Load Scheduling: A Case Study Based on Real Life Consumption Data

Author

Listed:
  • Reda El Makroum

    (School of Science and Engineering, Al Akhawayn University in Ifrane, Ifrane 53000, Morocco)

  • Ahmed Khallaayoun

    (School of Science and Engineering, Al Akhawayn University in Ifrane, Ifrane 53000, Morocco)

  • Rachid Lghoul

    (School of Science and Engineering, Al Akhawayn University in Ifrane, Ifrane 53000, Morocco)

  • Kedar Mehta

    (Institute of new Energy Systems (InES), Technische Hochschule Ingolstadt, 85051 Ingolstadt, Germany)

  • Wilfried Zörner

    (Institute of new Energy Systems (InES), Technische Hochschule Ingolstadt, 85051 Ingolstadt, Germany)

Abstract

This paper proposes a home energy management system able to achieve optimized load scheduling for the operation of appliances within a given household. The system, based on the genetic algorithm, provides recommendations for the user to improve the way the energy needs of the home are handled. These recommendations not only take into account the dynamic pricing of electricity, but also the optimization for solar energy usage as well as user comfort. Historical data regarding the times at which the appliances have been used is leveraged through a statistical method to integrate the user’s preference into the algorithm. Based on real life appliance consumption data collected from a household in Morocco, three scenarios are established to assess the performance of the proposed system with each scenario having different parameters. Running the scenarios on the developed MATLAB script shows a cost saving of up to 63.48% as compared to a base scenario for a specific day. These results demonstrate that significant cost saving can be achieved while maintaining user comfort. The addition of supplementary shiftable loads (i.e., an electric vehicle) to the household as well as the limitations of such home energy management systems are discussed. The main contribution of this paper is the real data and including the user comfort as a metric in in the home energy management scheme.

Suggested Citation

  • Reda El Makroum & Ahmed Khallaayoun & Rachid Lghoul & Kedar Mehta & Wilfried Zörner, 2023. "Home Energy Management System Based on Genetic Algorithm for Load Scheduling: A Case Study Based on Real Life Consumption Data," Energies, MDPI, vol. 16(6), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2698-:d:1096531
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    References listed on IDEAS

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    1. Shahin Bayramov & Iurii Prokazov & Sergey Kondrashev & Jan Kowalik, 2021. "Household Electricity Generation as a Way of Energy Independence of States—Social Context of Energy Management," Energies, MDPI, vol. 14(12), pages 1-19, June.
    2. Daud Mustafa Minhas & Josef Meiers & Georg Frey, 2022. "Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets," Energies, MDPI, vol. 15(5), pages 1-29, February.
    3. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
    4. Arshad Mohammad & Mohd Zuhaib & Imtiaz Ashraf & Marwan Alsultan & Shafiq Ahmad & Adil Sarwar & Mali Abdollahian, 2021. "Integration of Electric Vehicles and Energy Storage System in Home Energy Management System with Home to Grid Capability," Energies, MDPI, vol. 14(24), pages 1-27, December.
    5. Dileep, G., 2020. "A survey on smart grid technologies and applications," Renewable Energy, Elsevier, vol. 146(C), pages 2589-2625.
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    Cited by:

    1. Paweł Ziółkowski & Marta Drosińska-Komor & Jerzy Głuch & Łukasz Breńkacz, 2023. "Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence," Energies, MDPI, vol. 16(17), pages 1-28, August.
    2. Mohammed Qais & K. H. Loo & Hany M. Hasanien & Saad Alghuwainem, 2023. "Optimal Comfortable Load Schedule for Home Energy Management Including Photovoltaic and Battery Systems," Sustainability, MDPI, vol. 15(12), pages 1-15, June.
    3. Zurisaddai Severiche-Maury & Carlos Eduardo Uc-Rios & Wilson Arrubla-Hoyos & Dora Cama-Pinto & Juan Antonio Holgado-Terriza & Miguel Damas-Hermoso & Alejandro Cama-Pinto, 2025. "Forecasting Residential Energy Consumption with the Use of Long Short-Term Memory Recurrent Neural Networks," Energies, MDPI, vol. 18(5), pages 1-19, March.

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