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

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.

Automated summary

This paper presents a home energy management system that optimizes load scheduling for household appliances. Based on the genetic algorithm, the system provides recommendations to improve energy handling based on dynamic pricing, solar energy usage, and user comfort. By leveraging historical appliance usage data, user preferences are integrated into the algorithm. Simulation results based on real-life appliance consumption data show potential cost savings up to 63.48% while maintaining user comfort. The paper also discusses the inclusion of supplementary shiftable loads and limitations of such systems. The main contribution lies in using real data and considering user comfort as a metric in the energy management scheme.