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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Real-time Energy Management in Smart Homes through Deep Reinforcement Learning
AU - Aldahmashi, Jamal
AU - Ma, Xiandong
PY - 2024/4/1
Y1 - 2024/4/1
N2 - In light of the growing prevalence of distributed energy resources, energy storage systems (ESs), and electric vehicles (EVs) at the residential scale, home energy management (HEM) systems have become instrumental in amplifying economic advantages for consumers. These systems traditionally prioritize curtailing active power consumption, often at an expense of overlooking reactive power. A significant imbalance between active and reactive power can detrimentally impact the power factor in the home-to-grid interface. This research presents an innovative strategy designed to optimize the performance of HEM systems, ensuring they not only meet financial and operational goals but also enhance the power factor. The approach involves the strategic operation of flexible loads, meticulous control of thermostatic load in line with user preferences, and precise determination of active and reactive power values for both ES and EV. This optimizes cost savings and augments the power factor. Recognizing the uncertainties in user behaviors, renewable energy generations, and external temperature fluctuations, our model employs a Markov decision process for depiction. Moreover, the research advances a model-free HEM system grounded in deep reinforcement learning, thereby offering a notable proficiency in handling the multifaceted nature of smart home settings and ensuring real-time optimal load scheduling. Comprehensive assessments using real-world datasets validate our approach. Notably, the proposed methodology can elevate the power factor from 0.44 to 0.9 and achieve a significant 31.5% reduction in electricity bills, while upholding consumer satisfaction.
AB - In light of the growing prevalence of distributed energy resources, energy storage systems (ESs), and electric vehicles (EVs) at the residential scale, home energy management (HEM) systems have become instrumental in amplifying economic advantages for consumers. These systems traditionally prioritize curtailing active power consumption, often at an expense of overlooking reactive power. A significant imbalance between active and reactive power can detrimentally impact the power factor in the home-to-grid interface. This research presents an innovative strategy designed to optimize the performance of HEM systems, ensuring they not only meet financial and operational goals but also enhance the power factor. The approach involves the strategic operation of flexible loads, meticulous control of thermostatic load in line with user preferences, and precise determination of active and reactive power values for both ES and EV. This optimizes cost savings and augments the power factor. Recognizing the uncertainties in user behaviors, renewable energy generations, and external temperature fluctuations, our model employs a Markov decision process for depiction. Moreover, the research advances a model-free HEM system grounded in deep reinforcement learning, thereby offering a notable proficiency in handling the multifaceted nature of smart home settings and ensuring real-time optimal load scheduling. Comprehensive assessments using real-world datasets validate our approach. Notably, the proposed methodology can elevate the power factor from 0.44 to 0.9 and achieve a significant 31.5% reduction in electricity bills, while upholding consumer satisfaction.
KW - Adaptation models
KW - Deep reinforcement learning
KW - Electricity
KW - Energy consumption
KW - Energy management
KW - Home appliances
KW - Optimization
KW - Power factor correction
KW - Reactive power
KW - Real-time systems
KW - Scheduling
KW - Smart homes
KW - Uncertainty
KW - appliances scheduling
KW - deep reinforcement learning
KW - home energy management
KW - reactive power compensation
KW - smart homes
U2 - 10.1109/ACCESS.2024.3375771
DO - 10.1109/ACCESS.2024.3375771
M3 - Journal article
VL - 12
SP - 43155
EP - 43172
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -