As global energy demand increases and fossil fuel resources diminish, optimizing energy use in the residential sector has become essential. In Morocco, residential electricity consumption accounts for a significant portion of total demand, particularly during peak hours. This study explores the potential of intelligent scheduling of household appliances to optimize energy consumption, lower costs, and maintain occupant comfort. A survey conducted among Moroccan households assessed perceptions of automated appliance scheduling, focusing on comfort, energy efficiency, and system acceptability. The results highlight key appliances, such as refrigerators and water heaters, for optimization, while concerns about comfort arose with appliances like ovens and hairdryers. The study highlights the importance of adaptive systems that strike a balance between energy savings and user preferences, and suggests future research into intelligent systems that can adapt to user behaviors and seasonal changes.

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User Perception-Driven Energy Scheduling: A Human-Centered Strategy for Moroccan Households

  • Mohammed Ennejjar,
  • Mustapha Ezzini,
  • Mohammed Ali Jallal,
  • Samira Chabaa,
  • Abdelouhab Zeroual

摘要

As global energy demand increases and fossil fuel resources diminish, optimizing energy use in the residential sector has become essential. In Morocco, residential electricity consumption accounts for a significant portion of total demand, particularly during peak hours. This study explores the potential of intelligent scheduling of household appliances to optimize energy consumption, lower costs, and maintain occupant comfort. A survey conducted among Moroccan households assessed perceptions of automated appliance scheduling, focusing on comfort, energy efficiency, and system acceptability. The results highlight key appliances, such as refrigerators and water heaters, for optimization, while concerns about comfort arose with appliances like ovens and hairdryers. The study highlights the importance of adaptive systems that strike a balance between energy savings and user preferences, and suggests future research into intelligent systems that can adapt to user behaviors and seasonal changes.