Smart-valve-assisted model-free predictive control system for chiller plants

Lee, Dasheng; Lin, Chien‐Jung; Lai, Chih-Wei; Huang, Tsai Chi

doi:10.1016/j.enbuild.2020.110708

Cited by 12 publications

(2 citation statements)

References 44 publications

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“…The energy-saving effect at actual test sites was more than twice the 15.7% energy savings estimated from simulations [90] Chiller plant for a three-story building with an area of 9072 m 2 ; this building primarily serves as an office space and a semiconductor production line, and 70% of its energy consumption can be attributed to its air-conditioning system Real experiment in a factory Taiwan/Three-year test…”

Section: Hvac System Descriptionmentioning

confidence: 94%

Sustainable Air-Conditioning Systems Enabled by Artificial Intelligence: Research Status, Enterprise Patent Analysis, and Future Prospects

Lee

Chen

2022

Sustainability

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Artificial intelligence (AI) technologies have developed rapidly since 2000. Numerous academic papers have been published regarding energy efficiency improvements for air-conditioning systems. This study reviewed 12 review papers and selected 85 specific cases of applications of AI for HVAC energy usage reduction. In addition to academic studies, 31,221 patents related to HVAC energy-saving equipment filed by 11 companies were investigated. In order to analyze the large amount of data, this study developed a resource description framework (RDF) as an analysis tool. This tool was used with a natural language processing (NLP) program to compare the contents of academic papers and patents. With the automated analysis program, this study aimed to link academic research and corporate research and development, mainly the enterprise patent applications, to analyze the reasons why AI can effectively save energy. This represents a complete analysis of the current status of academic and industrial development. Six methods were identified to save energy effectively, including model-based predictive control (MPC), thermal comfort control, model-free predictive control, control optimization, multi-agent control (MAC), and knowledge-based system/rule set (KBS/RS)-based control. The energy savings of these methods were quantified to be 8.8–25.5%. These methods are widely covered by the examined corporate patent applications. After using NLP to retrieve patent keywords, the landscapes of enterprise patents were constructed and the future research directions were identified. It is concluded that 10 topics, including novel neural network designs, smartphone-assisted machine learning, and transfer learning, can be used to increase the energy-saving effects of AI and enable sustainable air-conditioning systems.

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Section: Hvac System Descriptionmentioning

confidence: 94%

Sustainable Air-Conditioning Systems Enabled by Artificial Intelligence: Research Status, Enterprise Patent Analysis, and Future Prospects

Lee

Chen

2022

Sustainability

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“…The application of RL to spacecraft thermodynamic systems is rarely reported in recent years, according to the authors' literature review. Lee [47] introduced a RL-based model-free predictive control structure for chiller plants. Qiu [48] provided an optimal operation solution of chillers by combining RL technique and expertise knowledge, aiming for a balance of power and indoor comfort.…”

Section: Introductionmentioning

confidence: 99%

Trigger-Based K-Band Microwave Ranging System Thermal Control with Model-Free Learning Process

et al. 2022

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Micron-level accuracy K-band microwave ranging in space relies on the stability of the payload thermal control on-board; however, large quantities of thermal sensors and heating devices around the deployed instruments consume the precious inner communication resources of the central computer. Another problem arises, which is that the payload thermal protection environment can deteriorate gradually through years operating. In this paper, a new trigger-based thermal system controller design is proposed, with consideration of spaceborne communication burden reduction and actuator saturation, which guarantees stable temperature fluctuations of microwave payloads in space missions. The controller combines a nominal constant sampling PID inner loop and a trigger-based outer loop structure under constraints of heating device saturation. Moreover, an iterative model-free reinforcement learning process is adopted that can approximate the estimation of thermal dynamic modeling uncertainty online. Via extensive experiment in a laboratory environment, the performance of the proposed trigger thermal control is verified, with smaller temperature fluctuations compared to the nominal control, and obvious efficiency in system communications. The online learning algorithm is also tested with deliberate thermal conditions that deviate from the original system—the results can quickly converge to normal when the thermal disturbance is removed. Finally, the ranging accuracy is tested for the whole system, and a 25% (RMS) performance improvement can be realized by using a trigger-based control strategy—about 2.2 µm, compared to the nominal control method.

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Artificial intelligence enabled energy-efficient heating, ventilation and air conditioning system: Design, analysis and necessary hardware upgrades

Lee

2023

Applied Thermal Engineering

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Smart-valve-assisted model-free predictive control system for chiller plants

Cited by 12 publications

References 44 publications

Sustainable Air-Conditioning Systems Enabled by Artificial Intelligence: Research Status, Enterprise Patent Analysis, and Future Prospects

Sustainable Air-Conditioning Systems Enabled by Artificial Intelligence: Research Status, Enterprise Patent Analysis, and Future Prospects

Trigger-Based K-Band Microwave Ranging System Thermal Control with Model-Free Learning Process

Artificial intelligence enabled energy-efficient heating, ventilation and air conditioning system: Design, analysis and necessary hardware upgrades

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