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Smart building energy demand response (DR) plays a paramount role in the decarbonization of energy use by leveraging different techniques that adjust energy consumption in response to fluctuating prices and demands. In this paper, we present an explainable AI-integrated DR optimization framework for smart buildings. This adaptive XAI-based DR approach intelligently forecasts energy demands and schedules the energy consumption of the buildings based on the forecasted demand patterns, grid conditions, and energy prices. This approach allows the stakeholders to make informed and optimal DR decisions based on the forecasted demand to maximize energy efficiency and user comfort while minimizing electricity costs. The simulation results demonstrate that the proposed approach promotes energy efficiency and user comfort.

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An Explainable AI-Based Demand Response Optimization Framework for Smart Buildings

  • 会议论文

  • 发表日期：

    2025年

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  • Muhammad Ibrar,
  • Hayla Nahom,
  • Abegaz Mohammed,
  • Sergio Márquez-Sánchez,
  • Javier Hernandez Fernandez,
  • Juan Manuel Corchado,
  • Aiman Erbad

摘要

Smart building energy demand response (DR) plays a paramount role in the decarbonization of energy use by leveraging different techniques that adjust energy consumption in response to fluctuating prices and demands. In this paper, we present an explainable AI-integrated DR optimization framework for smart buildings. This adaptive XAI-based DR approach intelligently forecasts energy demands and schedules the energy consumption of the buildings based on the forecasted demand patterns, grid conditions, and energy prices. This approach allows the stakeholders to make informed and optimal DR decisions based on the forecasted demand to maximize energy efficiency and user comfort while minimizing electricity costs. The simulation results demonstrate that the proposed approach promotes energy efficiency and user comfort.