检索结果-内蒙古大学图书馆

Indoor energy-saving strategy optimization based on deep reinforcement learning and ddpg algorithm

COMPUTING 2025年第1期107卷 1-23页

作者： Wan, Yan Zhai, Yujia Cui, Can Song, Dexuan Macau Univ Sci & Technol Fac Humanities & Arts Ave Wai Long Taipa 999078 Macao Peoples R China

In order to understand the indoor energy-saving strategy optimization method of ddpg algorithm in air conditioning and heating system. Systems, this study proposes an indoor energy-saving strategy optimization method based on deep reinforcement learning and ddpg algorithm. In response to the lack of existing intelligent methods in the field of indoor building energy conservation in China, this article first analyzes the factors affecting the energy consumption of refrigeration units, determines the direction of energy conservation, and sets energy-saving control parameters: chilled water outlet temperature, chilled water pump flow rate, cooling water inlet temperature, and cooling water pump flow rate. Secondly, based on the actual situation, the constraint conditions for each control parameter are formulated to determine the optimization objective of minimizing the energy consumption of the refrigeration unit. Then, based on the characteristic that energy-saving parameters are all continuous values, the Enhanced Deep Deterministic Policy Gradient (E-ddpg) algorithm is selected to solve the optimal parameters of control parameters for each load interval. The experimental results show that the algorithm converges from 600 scenarios, indicating that the actions taken by the algorithm from this point onwards can minimize the total energy consumption of the refrigeration unit. Specifically, the range of control parameters obtained is: chilled water outlet temperature To = [7.6, 8.7] degrees C, chilled water pump flow Ti = [25.4, 26.5] m3/h, cooling water inlet temperature Vo = [74.5, 88.4] degrees C, cooling water pump flow Vi = [90.1, 106.3] m3/h. Combining the deep reinforcement learning load prediction method to obtain the next load, the system control parameters are adjusted to the optimal situation in advance. The deep reinforcement learning air conditioning load prediction method has high accuracy in air conditioning load prediction, thereby achieving energy conse

关键词： Deep reinforcement learning ddpg algorithm Indoor energy-saving 68-XX 68Qxx

来源：评论

学校读者我要写书评

暂无评论

Autonomous trajectory planning method for hypersonic vehicles in glide phase based on ddpg algorithm

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2023年第8期237卷 1855-1867页

作者： Bao, Cunyu Wang, Peng He, Ruizhi Tang, Guojian Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Hunan Peoples R China

An autonomous optimal trajectory planning method based on the deep deterministic policy gradient (ddpg) algorithm of reinforcement learning (RL) for hypersonic vehicles (HV) is proposed in this paper. First, the trajectory planning problem is converted into a Markov Decision Process (MDP), and the amplitude of the bank angle is designated as the control input. The reward function of the MDP is set to minimize the trajectory terminal position errors with satisfying hard constraints. The deep neural network (DNN) is used to approximate the policy function and action-value function in the ddpg framework. The Actor network then computes the control input directly according to flight states. Using a limited exploration strategy, the optimal policy network would be considered fully trained when the reward value reached maximum convergence. Simulation results show that the policy network trained using a ddpg algorithm accomplishes 3-dimensional (3D) trajectory planning during the HV glide phase with high terminal precision and stable convergence. Additionally, the single step calculation time of the policy network occurs in near real time, which suggests great potential as an autonomous online trajectory planner. Monte Carlo experiments prove the strong robustness of the implementation of an autonomous trajectory planner under aerodynamic disturbances.

关键词： deep reinforcement learning ddpg algorithm hypersonic vehicle 3D trajectory planning glide phase

来源：评论

学校读者我要写书评

暂无评论

A ddpg algorithm for Portfolio Management 19

A DDPG Algorithm for Portfolio Management

引用

19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

作者： Lin, Fang Wang, Meiqing Liu, Rong Hong, Qianying Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China

ISBN: (纸本)9781728197241

The purpose of portfolio management is to select a variety of fmancial products to form a portfolio and then manage these portfolios to achieve the purpose of diversifying risk and improving efficiency. In this paper, the Deep Detenninisfic Policy Gradient (ddpg) algorithm with neural networks is used, new states, actions and reward functions are proposed. The empirical analysis shows that this paper's method performs better than the method of investing with Q-learning algorithm, equally-weighted method, investing all funds in risk-free assets, or investing all funds in stocks.

关键词： deep reinforcement learning ddpg algorithm portfolio management

来源：评论

学校读者我要写书评

暂无评论

A ddpg algorithm for Portfolio Management

A DDPG Algorithm for Portfolio Management

引用

第十九届分布式计算及其应用国际学术研讨会

作者： Fang Lin Meiqing Wang Rong Liu Qianying Hong College of Mathematics and Computer Science Fuzhou University

The purpose of portfolio management is to select a variety of financial products to form a portfolio and then manage these portfolios to achieve the purpose of diversifying risk and improving *** this paper,the Deep Deterministic Policy Gradient(ddpg) algorithm with neural networks is used,new states,actions and reward functions are *** empirical analysis shows that this paper's method performs better than the method of investing with Q-learning algorithm,equally-weighted method,investing all funds in risk-free assets,or investing all funds in stocks.

关键词： deep reinforcement learning ddpg algorithm portfolio management

来源：评论

学校读者我要写书评

暂无评论

Energy Storage Assisted Conventional Unit Load Frequency Control Strategy Using Deep Reinforcement Learning

引用

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2025年第3期20卷 1307-1319页

作者： Yang, Lunjin Fu, Rong Lin, Jinxing Xu, Fengyu Wu, Xiang Nanjing Univ Posts & Telecommun Coll Automat & Coll Artificial Intelligence Nanjing 210023 Peoples R China Guizhou Normal Univ Sch Math Sci Guiyang 550001 Peoples R China

The traditional load frequency control systems suffer from long response time lag of thermal power units, low climbing rate, and poor disturbance resistance ability. By introducing energy storage participation in secondary frequency regulation and a deep reinforcement learning technique, a new load frequency control strategy is proposed. Firstly, the rules for two operating modes of the energy storage, i.e., adaptive frequency regulation and energy storage self-recovery, are designed. Then, a deep reinforcement learning load frequency controller is designed to dynamically adjust the outputs of the energy storage system and the conventional unit. To improve the exploration efficiency of the deep reinforcement learning algorithm, a random network distillation technique is used. A multi-objective reward function containing an external reward and an additional internal reward is designed. Finally, simulation results show that, compared with the traditional load frequency control strategy, the proposed control strategy can achieve optimal performance in frequency regulation.

关键词： Load frequency control Energy storage SOC Reinforcement learning ddpg algorithm Random network distillation

来源：评论

学校读者我要写书评

暂无评论

Dynamic Pricing Strategy of Electric Vehicle Aggregators Based on ddpg Reinforcement Learning algorithm

引用

IEEE ACCESS 2021年 9卷 21556-21566页

作者： Liu, Dunnan Wang, Weiye Wang, Lingxiang Jia, Heping Shi, Mengshu North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China

The fixed service charge pricing model adopted by traditional electric vehicle aggregators (EVAs) is difficult to effectively guide the demand side resources to respond to the power market price signal. At the same time, real-time pricing strategy can flexibly reflect the situation of market supply and demand, shift the charging load of electric vehicles (EVs), reduce the negative impact of disorderly charging on the stable operation of power systems, and fully tap the economic potential of EVA participating in the power market. Based on the historical behavior data of EVs, this paper considers various market factors such as peak-valley time-of-use tariff, demand-side response mode and deviation balance of spot market to formulate the objective function of EVA comprehensive revenue maximization and establishes a quarter-hourly vehicle-to-grid (V2G) dynamic time-sharing pricing model based on deep deterministic policy gradient (ddpg) reinforcement learning algorithm. The EVA yield difference between peak-valley time-of-use tariff and hourly pricing strategy under the same algorithm is compared through the case studies. The results show that the scheme with higher pricing frequency can guide the charging behavior of users more effectively, tap the economic potential of power market to a greater extent, and calm the load fluctuation of power grid.

关键词： Pricing Heuristic algorithms Power markets Reinforcement learning Power grids Power system dynamics Vehicle dynamics Electric vehicle aggregator dynamic pricing ddpg algorithm charging behavior guidance

来源：评论

学校读者我要写书评

暂无评论

A 5G-TSN joint resource scheduling algorithm based on optimized deep reinforcement learning model for industrial networks

引用

AD HOC NETWORKS 2025年 170卷

作者： Zhang, Yang Sun, Lei Ma, Zhangchao Wang, Jianquan Fu, Meixia Joung, Jinoo Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Sangmyung Univ Dept Human Ctr Artificial Intelligence Seoul South Korea

As the Industrial Internet of Things (IIoT) evolves, the rapid growth of connected devices in industrial networks generates massive amounts of data. These transmissions impose stringent requirements on network communications, including reliable bounded latency and high throughput. To address these challenges, the integration of the fifth-generation (5G) mobile cellular networks and Time-Sensitive Networking (TSN) has emerged as a prominent solution for scheduling diverse traffic flows. While Deep Reinforcement Learning (DRL) algorithms have been widely employed to tackle scheduling issues within the 5G-TSN architecture, existing approaches often neglect throughput optimization in multi-user scenarios and the impact of Channel Quality Indicators (CQI) on resource allocation. To overcome these limitations, this study introduces ME-ddpg, a novel joint resource scheduling algorithm. ME-ddpg extends the Deep Deterministic Policy Gradient (ddpg) model by embedding a Modulation and Coding Scheme (MCS)-based priority scheme. This improvement in computational efficiency is critical for real-time scheduling in IIoT environments. Specifically, ME-ddpg provides latency guarantees for time-triggered applications, ensures throughput for video applications, and maximizes overall system throughput across 5 G and TSN domains. Simulation results demonstrate that the proposed ME-ddpg achieves 100 % latency reliability for time-triggered flows and improves system throughput by 10.84 % over existing algorithms under varying Gate Control List (GCL) configurations and user ratios. Furthermore, due to the combination of MCSbased resource allocation scheme with ddpg model, the proposed ME-ddpg achieves faster convergence speed of the reward function compared to the original ddpg method.

关键词： 5G-TSN network End-to-End scheduling ddpg algorithm Dynamic priority scheme

来源：评论

学校读者我要写书评

暂无评论

Implementation of ddpg-Based Reinforcement Learning Control for Self-Balancing Motorcycle

引用

IEEE ACCESS 2024年 12卷 117271-117284页

作者： Lakshmi, K. Vijaya Manimozhi, M. Vellore Inst Technol Sch Elect Engn Vellore 632014 Tamil Nadu India

This paper presents the implementation of a Deep Deterministic Policy Gradient (ddpg) algorithm in Reinforcement Learning (RL) for self-balancing a motorcycle. The ddpg agent iteratively interacts with the motorcycle environment to develop an optimal control policy, utilizing states such as position and velocity, and actions like motor torque. The study evaluates the performance through simulations and real-time experimentation, demonstrating the algorithm's effectiveness in balancing the motorcycle across various leaning angles and in handling external disturbances and model uncertainties. Comparative analysis with a traditional PD controller highlights ddpg's faster response times, improved disturbance rejection, and enhanced adaptability to uncertainties. The results underscore the potential of RL algorithms in enhancing motorcycle control systems for safer and more efficient operation.

关键词： Motorcycles Wheels Reinforcement learning Heuristic algorithms Control systems Vehicle dynamics PD control Arduino nano 33 IOT ddpg algorithm deep RL self-balancing motorcycle

来源：评论

学校读者我要写书评

暂无评论

ddpg-based control strategy for sedimentation process in mud of rake suction dredger 5

DDPG-based control strategy for sedimentation process in mud...

引用

5th International Conference on Computer Engineering and Application (ICCEA)

作者： Bao, Wei Su, Zhen Jiangsu Univ Sci & Technol Sch Comp Sci Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Aiming at the low yield problem caused by the serious reliance on manual experience operation in the mud deposition process of rake suction dredger, this paper proposes a control strategy based on the ddpg algorithm for the mud deposition process, which can realize the intelligent control of the mud deposition process according to the current construction *** the mechanism of sediment deposition process of rake suction dredger is analysed and modelled. Secondly, the ddpg algorithm is used to fully explore the process in a sediment deposition modelling environment. The experimental results show that the strategy reduces the overflow loss and increases the sediment deposition volume by adjusting the overflow barrel height, inlet flow rate and inlet density in real time to optimize the yield.

关键词： rake suction dredger reinforcement learning yield optimization ddpg algorithm

来源：评论

学校读者我要写书评

暂无评论

ddpg-based Resource Management for MEC/UAV-Assisted Vehicular Networks 92

DDPG-based Resource Management for MEC/UAV-Assisted Vehicula...

引用

92nd IEEE Vehicular Technology Conference (IEEE VTC-Fall)

作者： Peng, Haixia Shen, Xuemin Sherman Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781728194844

In this paper, we investigate joint vehicle association and multi-dimensional resource management in a vehicular network assisted by multi-access edge computing (MEC) and unmanned aerial vehicle (UAV). To efficiently manage the available spectrum, computing, and caching resources for the MEC-mounted base station and UAVs, a resource optimization problem is formulated and carried out at a central controller. Considering the overlong solving time of the formulated problem and the sensitive delay requirements of vehicular applications, we transform the optimization problem using reinforcement learning and then design a deep deterministic policy gradient (ddpg)-based solution. Through training the ddpg-based resource management model offline, optimal vehicle association and resource allocation decisions can be obtained rapidly. Simulation results demonstrate that the ddpg-based resource management scheme can converge within 200 episodes and achieve higher delay/quality-of-service satisfaction ratios than the random scheme.

关键词： Vehicular networks unmanned aerial vehicle multi-access edge computing resource management ddpg algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：