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In the realm of home energy management (HEM) system, the integration of demand response (DR) strategies with predictive modeling holds immense potential for optimizing consumption patterns. This article presents a comprehensive framework that combines reinforcement learning (RL) with predictive modeling using a Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM) architecture. The CNN-LSTM model is employed to forecast photovoltaic (PV) power production and electricity prices, crucial factors influencing energy consumption decisions. The proposed approach considers various household appliance, including non-shiftable, power-shiftable, time-shiftable and Electric mobility devices, each represented as an autonomous agent, enabling decentralized decision-making tailored to specific operational constraints. By employing RL techniques, the model facilitates intelligent control, prioritizing the minimization of energy costs while simultaneously taking into account user comfort and appliance functionality. The simulation results validate the effectiveness and resilience of the proposed algorithm, showcasing a remarkable 36.7% reduction in electricity bills without compromising consumer satisfaction.

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Data-Driven Home Energy Management Optimization Using Reinforcement Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Abdelaziz El Aouni,
  • Salah Eddine Naimi,
  • Yassine Ayat,
  • Ismail Mir

摘要

In the realm of home energy management (HEM) system, the integration of demand response (DR) strategies with predictive modeling holds immense potential for optimizing consumption patterns. This article presents a comprehensive framework that combines reinforcement learning (RL) with predictive modeling using a Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM) architecture. The CNN-LSTM model is employed to forecast photovoltaic (PV) power production and electricity prices, crucial factors influencing energy consumption decisions. The proposed approach considers various household appliance, including non-shiftable, power-shiftable, time-shiftable and Electric mobility devices, each represented as an autonomous agent, enabling decentralized decision-making tailored to specific operational constraints. By employing RL techniques, the model facilitates intelligent control, prioritizing the minimization of energy costs while simultaneously taking into account user comfort and appliance functionality. The simulation results validate the effectiveness and resilience of the proposed algorithm, showcasing a remarkable 36.7% reduction in electricity bills without compromising consumer satisfaction.