A data-driven DRL-based home energy management system optimization framework considering uncertain household parameters

Ren, Kezheng; Liu, Jun; Wu, Zeyang; Liu, Xinglei; Nie, Yongxin; Xu, Haitao

doi:10.1016/j.apenergy.2023.122258

Cited by 31 publications

(3 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scheduling strategies used in HEMS mainly include real-time energy allocation, day ahead scheduling, and closed-loop energy management. Among them, day ahead scheduling can reduce computational complexity and improve computational efficiency, which is widely accepted and applied (Ren et al, 2024).…”

Section: Open Accessmentioning

confidence: 99%

See 1 more Smart Citation

Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach

Pan,

Zhu,

Luo

et al. 2024

Front. Therm. Eng.

View full text Add to dashboard Cite

With the increase in the integration of renewable sources, the home energy management system (HEMS) has become a promising approach to improve grid energy efficiency and relieve network stress. In this context, this paper proposes an optimization dispatching strategy for HEMS to reduce total cost with full consideration of uncertainties, while ensuring the users’ comfort. Firstly, a HEMS dispatching model is constructed to reasonably schedule the start/stop time of the dispatchable appliances and energy storage system to minimize the total cost for home users. Besides, this dispatching strategy also controls the switching time of temperature-controlled load such as air conditioning to reduce the energy consumption while maintaining the indoor temperature in a comfortable level. Then, the optimal dispatching problem of HEMS is modeled as a Markov decision process (MDP) and solved by a deep reinforcement learning algorithm called deep deterministic policy gradient. The example results verify the effectiveness and superiority of the proposed method. The energy cost can be effectively reduced by 21.9% at least compared with other benchmarks and the indoor temperature can be well maintained.

show abstract

Section: Open Accessmentioning

confidence: 99%

“…However, the optimization results calculated by RO method are usually conservative and utilize only one dispatch solution to deal with all uncertainties of whole dispatch period. To this end, the learning-based methods have been utilized to solve this problem (Hafeez et al, 2020b;Ben Slama and Mahmoud, 2023;Ren et al, 2024).…”

Section: Related Workmentioning

confidence: 99%

Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach

Pan,

Zhu,

Luo

et al. 2024

Front. Therm. Eng.

View full text Add to dashboard Cite

show abstract

“…The agent adjusts the recommended products to suit the user's personalized needs by observing the user's shopping history and environmental changes. In addition, DRL can handle the temporal and dynamic nature of user behavior and better capture users' changing preferences during shopping (Ren et al, 2024). Through interactive learning with the environment, DRL can provide personalized product recommendations and achieve a smarter shopping experience to meet the changing needs of users.…”

mentioning

confidence: 99%

Deep Learning and User Consumption Trends Classification and Analysis Based on Shopping Behavior

Liu,

Hou,

Zhao

2024

Journal of Organizational and End User Computing

View full text Add to dashboard Cite

Driven by the wave of digitalization, the booming development of the e-commerce industry urgently requires in-depth analysis of user shopping behavior to improve service experience. In view of the limitations of traditional models in dealing with complex shopping scenarios, this study innovatively proposes a deep learning model: the VATA model (a combination of variational autoencoder, transformer, and attention mechanism). Through this model, the authors strive to classify and analyze user shopping behavior more accurately and intelligently. Variational autoencoder (VAE) can learn the potential representation of user personalized historical data, capture the implicit characteristics of shopping behavior, and improve the ability to deal with actual shopping situations. Transformer models can more comprehensively capture the dependencies between shopping behaviors and understand shopping. The overall structure of behavior plays an important role.

show abstract

Hybrid self-attention BiLSTM and incentive learning-based collaborative filtering for e-commerce recommendation systems

Abdalla,

Gheisari,

Awlla

2024

Electron Commer Res

View full text Add to dashboard Cite

A data-driven DRL-based home energy management system optimization framework considering uncertain household parameters

Cited by 31 publications

References 46 publications

Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach

Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach

Deep Learning and User Consumption Trends Classification and Analysis Based on Shopping Behavior

Hybrid self-attention BiLSTM and incentive learning-based collaborative filtering for e-commerce recommendation systems

Contact Info

Product

Resources

About