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Nature-inspired solutions for energy sustainability using novel optimization methods.

Abdulwahab Ali Almazroi1, Ch Anwar Ul Hassan2

  • 1Department of Information Technology, College of Computing and Information Technology at Khulais, Univeristy of Jeddah, Jeddah, Saudi Arabia.

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|November 27, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a Home Electricity Management (HEM) system using meta-heuristics like the Strawberry Algorithm (SWA) and Social Spider Algorithm (SSA) for efficient energy use. The HEM system significantly cuts residential electricity costs and reduces peak power demand, enhancing the home energy supply chain.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Operations Research

Background:

  • The increasing demand for electricity and the complexity of the modern home energy supply chain necessitate efficient management systems.
  • Intelligent home gadgets require optimized scheduling to balance energy consumption and reduce costs.
  • Existing meta-heuristics for energy management show limitations in cost savings and peak load reduction.

Purpose of the Study:

  • To develop and evaluate a Home Electricity Management (HEM) system for optimizing residential energy consumption.
  • To compare the effectiveness of the Social Spider Algorithm (SSA) and Strawberry Algorithm (SWA) in managing home energy.
  • To assess the impact of the HEM system on cost savings, peak-to-average power ratio (PAR), and carbon emissions.

Main Methods:

  • Development of a Home Electricity Management (HEM) system incorporating advanced meta-heuristics.
  • Implementation and simulation of the Social Spider Algorithm (SSA) for peak-to-average power ratio reduction.
  • Implementation and simulation of the Strawberry Algorithm (SWA) for cost savings optimization.
  • Comparative analysis of SSA and SWA against existing meta-heuristics and baseline scenarios.

Main Results:

  • The Strawberry Algorithm (SWA) demonstrated superior performance in achieving cost savings, reducing residential electricity expenses by up to 3.5%.
  • The Social Spider Algorithm (SSA) proved more effective in reducing the peak-to-average power ratio (PAR), indicating better load balancing.
  • The proposed HEM system outperformed existing meta-heuristics in both cost reduction and PAR improvement.

Conclusions:

  • The developed HEM system offers a practical and sustainable solution for managing home electricity consumption within the supply chain.
  • The system provides significant economic benefits through cost savings and environmental advantages via reduced carbon emissions.
  • The research highlights the potential of meta-heuristic algorithms like SWA and SSA to enhance efficiency and sustainability in the residential energy sector.