Home Energy Recommendation System (HERS): A Deep Reinforcement Learning Method Based on Residents’ Feedback and Activity

Shuvo, Salman Sadiq; Yılmaz, Yasin

doi:10.1109/tsg.2022.3158814

Cited by 37 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this challenging problem with continuous state space and high-dimensional action space, policy gradient RL methods provide an effective solution approach. Specifically, the Advantage Actor-Critic (A2C) algorithm is known to provide quick convergence in such problems [4], [18], [19], [22], [26], [27]. Using two neural networks (actor and critic) A2C reduces the variance of its predecessor policy gradient algorithm the REINFORCE [26].…”

Section: F Solution Approachmentioning

confidence: 99%

“…Numerous existing works proposed scheduling techniques for the time shiftable appliances (e.g., Dishwasher, washer dryer, EV charging, etc.) of a household to capitalize the TOU tariffs [4]- [6]. Although such DSM techniques can flatten the demand curve to an extent, they do not sufficiently address the increasing stress of EV charging on the distribution transformers since they lack the utility-side management of the problem.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Demand-Side and Utility-Side Management Techniques for Increasing EV Charging Load

Shuvo

Yılmaz

2023

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

Section: F Solution Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Demand-Side and Utility-Side Management Techniques for Increasing EV Charging Load

Shuvo

Yılmaz

2023

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

“…In [88], the building is regarded as a whole, and action is the temperature change from the original schedule. In [91] and [92], RL-based HEM methods are proposed. Environment contains models of various appliances and interaction rules with the grid.…”

Section: Intelligent Load Managementmentioning

confidence: 99%

Application of reinforcement learning in planning and operation of new power system towards carbon peaking and neutrality

Sun¹,

Wang²,

Huang³

et al. 2023

Prog. Energy

View full text Add to dashboard Cite

To mitigate global climate change and ensure a sustainable energy future, China has launched a new energy policy of achieving carbon peaking by 2030 and carbon neutrality by 2060, which sets an ambitious goal of building New Power System (NPS) with high penetration of renewable energy. However, the strong uncertainty, nonlinearity, and intermittency of renewable generation and their power electronics-based control devices are imposing grand challenges for secure and economic planning and operation of the NPS. The performance of traditional methods and tools becomes rather limited under such phenomena. Together with high-fidelity modeling and high-performance simulation techniques, the fast development of artificial intelligence (AI) technology, especially reinforcement learning (RL), provides a promising way of tackling these critical issues. This paper first provides a comprehensive overview of RL methods that interact with high-fidelity grid simulators to train effective agents for intelligent, model-free decision-making. Secondly, three important applications of RL are reviewed, including device-level control, system-level optimized control, and demand side management, with detailed modeling and procedures of solution explained. Finally, this paper discusses future research efforts for achieving the goals of full absorption of renewable energy, optimized allocation of large-scale energy resources, reliable supply of electricity, and secure and economic operation of the power grid.

show abstract

“…Yu et al (2019) suggested also a model using MDP to schedule optimally HVAC appliances and the energy storage system of a smart home. Finally, Shuvo and Yilmaz (2022), proposed a DFL model that incorporated human feedback in the objective function and human activity data in the reinforcement learning part of it to enhance optimization of energy.…”

Section: Deep Learningmentioning

confidence: 99%

Intelligent Energy Management Systems -- A Review

Mischos¹,

Eleanna²,

Vrakas³

2022

Preprint

View full text Add to dashboard Cite

Climate change has become a major problem for humanity in the last two decades. One of the reasons that caused it, is our daily energy waste. People consume electricity in order to use home/work appliances and devices and also reach certain levels of comfort while working or being at home. However, even though the environmental impact of this behavior is not immediately observed, it leads to increased CO2 emissions coming from energy generation from power plants. Confronting such a problem efficiently will affect both the environment and our society. Monitoring energy consumption in real-time, changing energy wastage behavior of occupants and using automations with incorporated energy savings scenarios, are ways to decrease global energy footprint. In this review, we study intelligent systems for energy management in residential, commercial and educational buildings, classifying them in two major categories depending on whether they provide direct or indirect control. The article also discusses what the strengths and weaknesses are, which optimization techniques do they use and finally, provide insights about how these systems can be improved in the future.

show abstract

Home Energy Recommendation System (HERS): A Deep Reinforcement Learning Method Based on Residents’ Feedback and Activity

Cited by 37 publications

References 39 publications

Demand-Side and Utility-Side Management Techniques for Increasing EV Charging Load

Demand-Side and Utility-Side Management Techniques for Increasing EV Charging Load

Application of reinforcement learning in planning and operation of new power system towards carbon peaking and neutrality

Intelligent Energy Management Systems -- A Review

Contact Info

Product

Resources

About