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Intelligent Operation Strategies for PVT-ASHP Heating and Hot Water Systems in Industrial Parks Based on Reinforcement Learning 认领 引用
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作者 Yingjie Su Yubin Qiu +2 位作者 Zhuojun Dong Jiying Liu Bo Gao 《Energy Engineering》 EI 2026年第7期40-74,共35页
In response to the high energy consumption,large load fluctuations,and insufficient adaptability associated with conventional control strategies in industrial park heating and hot water systems,this paper studies a 15... In response to the high energy consumption,large load fluctuations,and insufficient adaptability associated with conventional control strategies in industrial park heating and hot water systems,this paper studies a 15,000 m2 factory office building in Jinan as its object of study.A photovoltaic-thermal integrated air-source heat pump system(PVT-ASHP)is developed.This system leverages its hardware parameter co-optimization and intelligent operational strategy control to perform cost reduction and efficiency increase,while focusing on the novel innovative high effectiveness of its operational strategies.The study first employs the Hooke-Jeeves algorithm to optimize key hardware parameters so as to minimize the annual cost,perform many adjustments,including the reduction of the PVT collector area from 931 to 799 m2,regulate the PVT tilt angle from 36°to 43°,and modify the storage tank volume.This allows for the establishment of a low-energy baseline,reducing the initial PVT equipment investment by approximately 14.2%.In addition,the PVT photovoltaic efficiency is stabilized at 14%,while the solar thermal efficiency fluctuates around 33%.The core operational strategy uses a reinforcement learning algorithm based on Deep Q-Network(DQN).Its design incorporates dual variables PVT electricity generation(PVTd)and PVT heat supply(PVTh)into the state space,which overcomes the limitations of conventional control relying solely on load and outdoor temperature to perform dynamic matching between energy production and load demand.The reward function comprises dynamic weighting for energy consumption and comfort,where the energy consumption weight and comfort weight are set to 0.9 and O.l,respectively.Based on the office hours of the factory(8:0o-18:0o as high load,and non-office hours as low load),an hourly load input mechanism is designed to remove the control deviations caused by the average load assumption.Simulations are then conducted.The obtained results demonstrate that,compared with the conventional control strategy of fixed temperature at 6OoC,the designed DQN reinforcement learning operation strategy achieves energy savings of about 2.99%.During office hours,the system maintains a stable supply water temperature of 57°C,which is consistent with comfort requirements while avoiding energy waste.After performing parameter optimization using the operational control strategy,the annual operating costs of the system decrease by 9.43%,while significantly increasing the overall energy efficiency.This paper demonstrates that the proposed DQN reinforcement learning operation strategy,tailored to the load characteristics of factory campuses,plays an important role in improving system performance.Based on the principles of dynamic perception,precise matching,and demand-driven regulation,it provides a potential reference framework for designing similar systems to ensure the efficient operation of distributed energy systems in factory campus-type buildings. 展开更多
关键词 Transient system simulation program Deep Q-Network Hooke-Jeeves algorithm generic optimization program heat supply
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Open Volumetric Mesh-An Efficient Data Structure for Tetrahedral and Hexahedral Meshes 认领 引用
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作者 XIAN Chu-hua LI Gui-qing GAO Shu-ming 《Computer Aided Drafting,Design and Manufacturing》 2013年第3期23-27,共5页
This work introduces a scalable and efficient topological structure for tetrahedral and hexahedral meshes. The design of the data structure aims at maximal flexibility and high performance. It provides a high scalabil... This work introduces a scalable and efficient topological structure for tetrahedral and hexahedral meshes. The design of the data structure aims at maximal flexibility and high performance. It provides a high scalability by using hierarchical representa-tions of topological elements. The proposed data structure is array-based, and it is a compact representation of the half-edge data structure for volume elements and half-face data structure for volumetric meshes. This guarantees constant access time to the neighbors of the topological elements. In addition, an open-source implementation named Open Volumetric Mesh （OVM） of the pro-posed data structure is written in C＋＋ using generic programming concepts. 展开更多
关键词 tetrahedral mesh hexahedral mesh geometry modeling data structure generic programming
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