PLC-Informed Distributed Game Theoretic Learning of Energy-Optimal Production Policies

Schwung, Dorothea; Yuwono, Steve; Schwung, Andreas; Ding, Steven X.

doi:10.1109/tcyb.2022.3179950

Cited by 8 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GT is a popular topic in economic studies and expands to the engineering field because of its capability to solve decision-making problems between multiple players under specific circumstances. A GT-based approach can be an alternative for RL since it typically requires less complexity and computation in the ML models optimization compared to deep RL [17,18]. The advantages make the approach much more applicable to real industrial environments.…”

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

MLPro — An integrative middleware framework for standardized machine learning tasks in Python

Arend¹,

Diprasetya²,

Yuwono³

et al. 2022

Software Impacts

Self Cite

View full text Add to dashboard Cite

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

MLPro — An integrative middleware framework for standardized machine learning tasks in Python

Arend¹,

Diprasetya²,

Yuwono³

et al. 2022

Software Impacts

Self Cite

View full text Add to dashboard Cite

“…Among various communication technologies, fiber optic technology cannot adapt to the wide coverage requirement of IoT due to the high deployment cost (Chagnon, 2019;Liao et al, 2023b;Lin et al, 2023). Wireless fidelity (WiFi) relying on the industrial, scientific, and medical (ISM) band is susceptible to interference and privacy issues (Schwung et al, 2023). In comparison, 5G and power line communication (PLC) emerge as feasible candidates (López et al, 2019;Pal et al, 2021;Zhou et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Delay and cost-balanced communication resource management for IoT-empowered distribution grid energy dispatch

Shi

2024

Front. Energy Res.

View full text Add to dashboard Cite

The combination of internet of things (IoT), 5G, and power line communication (PLC) provides real-time and low-cost data transmission services to meet the quality of service (QoS) requirements for distribution grid energy dispatch. However, the IoT-empowered communication resource management system faces challenges in optimizing delay and traffic cost for distribution grid energy dispatch. There is a contradiction between the long-term performance guarantee and short-term optimization objectives, compounded by competition for communication resources. In this paper, we construct the minimization problem of the weighted sum of data transmission delay and traffic cost. Utilizing Lyapunov optimization, we aim to decouple the long-term constraint of average queuing delay with the short-term optimization objective. Then, a delay and cost-balanced communication resource management algorithm based on two-layer iterative matching is proposed. It optimizes the communication mode selection by I-to-2 bidding matching in a large timescale and subchannel allocation by many-to-many deferred acceptance matching in a small timescale. The simulation data present that the proposed algorithm excels in reducing data transmission delay, minimizing traffic cost, and decreasing queuing delay.

show abstract

Model-based learning on state-based potential games for distributed self-optimization of manufacturing systems

Yuwono,

Schwung

2023

Journal of Manufacturing Systems

View full text Add to dashboard Cite

PLC-Informed Distributed Game Theoretic Learning of Energy-Optimal Production Policies

Cited by 8 publications

References 27 publications

MLPro — An integrative middleware framework for standardized machine learning tasks in Python

MLPro — An integrative middleware framework for standardized machine learning tasks in Python

Delay and cost-balanced communication resource management for IoT-empowered distribution grid energy dispatch

Model-based learning on state-based potential games for distributed self-optimization of manufacturing systems

Contact Info

Product

Resources

About