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

Reda El Makroum, Ahmed Khallaayoun, Rachid Lghoul, Kedar Mehta () and Wilfried Zörner
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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

Energies, 2023, vol. 16, issue 6, 1-18

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.

Keywords: home energy management; load scheduling; genetic algorithm; user comfort (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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