Reinforcement Learning-Based Dynamic Scheduling Algorithm for User-Side Multi-Source Data in Digital Electrical Rooms

With the increasing complexity and dynamics of digital electrical rooms, effective management of diverse data sources faces significant challenges. In this study, a dynamic scheduling algorithm based on reinforcement learning for user-side multi-source data in digital powerhouses is proposed, aiming...

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Vydáno v:2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC) s. 758 - 762
Hlavní autoři: Zhu, Wanting, Li, Jincan, Bi, Haipeng, Li, Pei, Deng, Dongdong, Qin, Zongtao
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 14.08.2024
Témata:
digital electrical room
Dynamic scheduling
dynamic scheduling algorithm
Heuristic algorithms
multi-source data
Power system stability
Real-time systems
Reinforcement learning
Resource management
resource optimization
Stability analysis
Sustainable development
System performance
Thermal stability
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Shrnutí:With the increasing complexity and dynamics of digital electrical rooms, effective management of diverse data sources faces significant challenges. In this study, a dynamic scheduling algorithm based on reinforcement learning for user-side multi-source data in digital powerhouses is proposed, aiming to optimize resource allocation and improve system performance. The algorithm utilizes reinforcement learning techniques to adaptively adjust scheduling strategies based on real-time environmental feedback. We outline the framework and implementation of the algorithm, highlighting its potential to cope with the dynamic nature of digital powerhouses and effectively utilize multi-source data. Through comprehensive experiments and analysis, we demonstrate the effectiveness and robustness of the proposed algorithm in various scenarios. Our findings emphasize the potential role of reinforcement learning in enhancing the efficiency and reliability of digital electrical room management systems.
DOI:10.1109/PEEEC63877.2024.00142