Optimized energy consumption model for smart home using improved differential evolution algorithm

Ima O. Essiet, Yanxia Sun, Zenghui Wang

Research output: Contribution to journalArticlepeer-review

64 Citations (Scopus)


This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other well-performing evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms’ ability to optimize a multi-objective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio.

Original languageEnglish
Pages (from-to)354-365
Number of pages12
Publication statusPublished - 1 Apr 2019


  • Demand response
  • Evolutionary algorithms
  • HEMS
  • Optimization
  • RES

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Modeling and Simulation
  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Pollution
  • General Energy
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Management, Monitoring, Policy and Law
  • Electrical and Electronic Engineering


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