Joint optimization for temperature and humidity independent control system based on multi-agent reinforcement learning with cooperative mechanisms
Applied Energy, ISSN: 0306-2619, Vol: 375, Page: 123968
2024
- 1Citations
- 7Captures
- 1Mentions
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Most Recent News
Findings from Tsinghua University in the Area of Technology Described (Joint Optimization for Temperature and Humidity Independent Control System Based On Multi-agent Reinforcement Learning With Cooperative Mechanisms)
2024 DEC 02 (NewsRx) -- By a News Reporter-Staff News Editor at Tech Daily News -- Current study results on Technology have been published. According
Article Description
The coupling of indoor thermal environment parameters and the complexity of subsystem control pose challenges to further enhancing the energy efficiency of air conditioning systems. This study adopts a multi-agent deep reinforcement learning algorithm for optimal control of a temperature and humidity independent control system combining radiant ceiling cooling with desiccant dehumidification. In the co-simulation process, Python completes the algorithm construction and employs the system and building models constructed by EnergyPlus to achieve the training of agents. This study aims to analyse the optimisation effects of different algorithms, discuss the behavioural characteristics among multiple agents during the training process, and reveal the impact of humidity intelligent control on the energy saving and energy-use flexibility effects. Compared to conventional single-agent optimization control methods, this study integrates interdependent control actions and leverages a cooperative mechanism to enhance system optimization. Simulation results demonstrate that the method can avoid the local cognitive limitations of individual agents in dynamic environments, achieving multi-objective optimization. Notably, the humidity control agent autonomously reduces humidity to assist in lowering the supply water temperature, thereby managing high sensible heat loads while avoiding condensation risks of the radiant cooling panels. The optimized strategy reduces chiller energy consumption by 41.4%, with 16% of this reduction attributed to humidity control. Furthermore, humidity control enables the chiller to operate at varying power levels, enhancing the energy-use flexibility. This study provides a replicable and transferable solution for multi-subsystem optimal control.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0306261924013515; http://dx.doi.org/10.1016/j.apenergy.2024.123968; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85201003905&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0306261924013515; https://dx.doi.org/10.1016/j.apenergy.2024.123968
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know