What question did this study set out to answer?

To develop a model for optimizing load distribution in thermal power plants using deep reinforcement learning techniques.

April 1, 2026工程管理与技术探讨Open Access

基于深度强化学习的火电厂机组负荷优化分配研究

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Authors

乔乔仙保刚王刚王瑞马瑞马

Discussion

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Overview

Demonstrates improved load distribution in thermal power plants, suggesting deep reinforcement learning offers significant advantages over traditional methods.

Key Points

To develop a model for optimizing load distribution in thermal power plants using deep reinforcement learning techniques.
Analyzed objectives and constraints of load distribution in thermal power plants.
Constructed a deep reinforcement learning model for load optimization.
Defined overall architecture, state space, action space, and reward function for the model.
Compared the DDPG algorithm implementation with traditional algorithms in simulations.
The deep reinforcement learning model outperformed traditional algorithms in real-time responsiveness.
Demonstrated significant advantages in handling dynamic working conditions.
Achieved efficient and rational load distribution for power plant units.

Cite This Study

乔仙保 et al. (2026) studied this question.

synapsesocial.com/papers/69cd79e15652765b073a6b11 https://doi.org/10.37155/2717-5189-0802-36

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April 1, 2026工程管理与技术探讨Open Access

基于深度强化学习的火电厂机组负荷优化分配研究

View Full Paper

Ask AI

Bookmark

Authors

乔乔仙保刚王刚王瑞马瑞马

Discussion

Member takes

Overview

Demonstrates improved load distribution in thermal power plants, suggesting deep reinforcement learning offers significant advantages over traditional methods.

Key Points

To develop a model for optimizing load distribution in thermal power plants using deep reinforcement learning techniques.
Analyzed objectives and constraints of load distribution in thermal power plants.
Constructed a deep reinforcement learning model for load optimization.
Defined overall architecture, state space, action space, and reward function for the model.
Compared the DDPG algorithm implementation with traditional algorithms in simulations.
The deep reinforcement learning model outperformed traditional algorithms in real-time responsiveness.
Demonstrated significant advantages in handling dynamic working conditions.
Achieved efficient and rational load distribution for power plant units.

Cite This Study

乔仙保 et al. (2026) studied this question.

synapsesocial.com/papers/69cd79e15652765b073a6b11 https://doi.org/10.37155/2717-5189-0802-36

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