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第43届中国控制会议

作者： Wei Fu Xianlian Wang Zhixiao Wang Sen Chen Cheng Zhao National Engineering Research Center of Power Generation Control and Safety School of Energy and EnvironmentSoutheast University School of Mathematics and Statistics Shaanxi Normal University Academy of Mathematics and systems Science Chinese Academy of Sciences

The cogeneration technology of photovoltaic(PV) coupled fuel cells holds significant potential for widespread application in low-carbon building energy ***,the thermal-electric coordinated scheduling of this system faces challenges related to multi-energy flows,strong coupling,and source-load *** address these issues,this paper establishes a novel multi-energy coupling model for the system and formulates an optimization problem with the objective of minimizing intraday comprehensive cost,subject to constraints on thermal-electric balances and device storage *** training under uncertain scenarios,this paper studies a deep reinforcement learning algorithm,specifically twin delayed deep deterministic policy gradient(TD3),enabling rapid evaluation of system states and facilitating decision-making for *** results under a typical winter day scenario demonstrate that the TD3-based scheduling method promoting the electric-thermal decoupling,reducing operation cost and enhancing device ***,compared to the rulebased method,the better performance and millisecond-level scheduling time highlighting TD3's real-time optimization ability,effectively enhancing the system's robustness and adaptability to uncertainty.

关键词： building energy system Deep reinforcement learning Fuel cell combined heat and power

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An interpretable graph convolutional neural network based fault diagnosis method for building energy systems

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building Simulation 2024年第7期17卷 1113-1136页

作者： Guannan Li Zhanpeng Yao Liang Chen Tao Li Chengliang Xu School of Urban Construction Wuhan University of Science and TechnologyWuhan 430065China Anhui Province Key Laboratory of Intelligent building and building energy-saving Anhui Jianzhu UniversityHefei 230601China State Key Laboratory of Green building in Western China Xi'an University of Architecture&TechnologyXi'an 710055China Key Laboratory of Low-grade energy Utilization Technologies and systems(Chongqing University) Ministry of Education of ChinaChongqing UniversityChongqing 400044China Hubei Provincial Engineering Research Centre of Urban Regeneration Wuhan University of Science and TechnologyWuhan 430081China

Due to the fast-modeling speed and high accuracy,deep learning has attracted great interest in the field of fault diagnosis in building energy systems in recent ***,the black-box nature makes deep learning models generally difficult to *** order to compensate for the poor interpretability of deep learning models,this study proposed a fault diagnosis method based on interpretable graph neural network(GNN)suitable for building energy *** method is developed by following three main steps:(1)selecting NC-GNN as a fault diagnosis model for building energy systems and proposing a graph generation method applicable to the model,(2)developing an interpretation method based on InputXGradient for the NC-GNN,which is capable of outputting the importance of the node features and automatically locating the fault related features,(3)visualizing the results of model interpretation and validating by matching with expert knowledge and maintenance *** was performed using the public ASHRAE RP-1043 chiller fault *** diagnosis results show that the proposed method has a diagnosis accuracy of over 96%.The interpretation results show that the method is capable of explaining the decision-making process of the model by identifying fault-discriminative *** almost all seven faults,their fault-discriminative features were correctly identified.

关键词： fault diagnosis graph neural network building energy system InputXGradient feature interpretation

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Challenges and opportunities of machine learning control in building operations

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building Simulation 2023年第6期16卷 831-852页

作者： Liang Zhang Zhelun Chen Xiangyu Zhang Amanda Pertzborn Xin Jin The University of Arizona 1209 E 2nd StTucsonAZUSA Drexel University 3141 Chestnut StPhiladelphiaPAUSA National Renewable energy Laboratory 15013 Denver W PkwyGoldenCOUSA National Institute of Standards and Technology 100 Bureau DrGaithersburgMDUSA

Machine learning control(MLC)is a highly flexible and adaptable method that enables the design,modeling,tuning,and maintenance of building controllers to be more accurate,automated,flexible,and *** research topic of MLC in building energy systems is developing rapidly,but to our knowledge,no review has been published that specifically and systematically focuses on MLC for building energy *** paper provides a systematic review of MLC in building energy *** review technical papers in two major categories of applications of machine learning in building control:(1)building system and component modeling for control,and(2)control process *** identify MLC topics that have been well-studied and those that need further research in the field of building operation *** also identify the gaps between the present and future application of MLC and predict future trends and opportunities.

关键词： machine learning building operation control building energy system reinforcement learning

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