Scholarlink

<p>Energy management is a crucial component of modern smart grid systems to ensure balance between supply-demand chains of power. The integration of complex structures for monitoring as well as existing techniques has enabled individual clients for actively participating in energy management programs. Thus, the given paper introduces an efficient approach to propose an energy management system for scheduling residential electricity loads. The proposed approach leverages fuzzy logic (FL) and whale optimization algorithm (WOA), incorporating consumption categories for operating household equipment during specific intervals. By adopting this strategy, the approach achieves significant reductions in peak electricity demand as well as lowers the electricity costs with regard to consumers. Additionally, the proposed approach attains the lowest mean average percentage error (MAPE %) and lowest power consumption (KWh) thereby surpassing existing studies in terms of performance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Utilization of fuzzy logic (FL) and whale optimization algorithm (WOA) for optimization of load scheduling in energy management systems

  • 期刊论文

  • 发表日期：

    2025年6月4日

查看全文
  • Bhivraj Suthar,
  • Ankit Punia,
  • Prabhat Sharma,
  • Alok Jain,
  • Ram Narayan,
  • Y. Kamala Raju,
  • Nidhi Sharma,
  • Ramy Riad Hussein,
  • Khristina Maksudovna Vafaeva

摘要

Energy management is a crucial component of modern smart grid systems to ensure balance between supply-demand chains of power. The integration of complex structures for monitoring as well as existing techniques has enabled individual clients for actively participating in energy management programs. Thus, the given paper introduces an efficient approach to propose an energy management system for scheduling residential electricity loads. The proposed approach leverages fuzzy logic (FL) and whale optimization algorithm (WOA), incorporating consumption categories for operating household equipment during specific intervals. By adopting this strategy, the approach achieves significant reductions in peak electricity demand as well as lowers the electricity costs with regard to consumers. Additionally, the proposed approach attains the lowest mean average percentage error (MAPE %) and lowest power consumption (KWh) thereby surpassing existing studies in terms of performance.