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Explainable reinforcement learning for enhancing personal thermal comfort and optimizing demand response in household multi-zone HVAC system

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Science China Technological Sciences 2025年第06期 121-137页

作者： Yongxin SU Mengyao XU Xiao LIU Mao TAN Rui WANG Chunhua YANG School of Automation and Electronic Information Xiangtan University Hunan National Center for Applied Mathematics Xiangtan University College of Systems Engineering National University of Defense Technology School of Automation Central South University

Heating, ventilation and air conditioning(HVAC) systems maintain personal thermal comfort(PTC) and serve as key demand response(DR) resources. Given the challenges of uncertain environments, varied user preferences, and legal requirements for transparent control strategies, this paper proposes an explainable reinforcement learning(XRL) solution for household multizone HVAC systems. This approach aims to optimize energy costs, ensure users' PTC, and maintain the explainability of the DR strategy under uncertainty. Firstly, an XRL-based optimization framework is proposed. The framework utilizes XRL's online learning capabilities to handle uncertainties and meet the PTC requirements of different zones while maintaining the explainability of the optimization strategy. Then, we propose an explainable proximal policy optimization(XPPO) algorithm as an instantiation of XRL for optimizing household multi-zone HVAC systems, using interpretable continuous control trees as actor networks of the XPPO. Moreover, we design state space, action space, reward, learning network, and learning algorithm of the XPPO in detail for the needs of HVAC operational optimization. Simulation results show that our method is explainable and thus can fulfill the requirements of the laws. At the same time, the proposed method consumes 22.4% less energy cost compared to scenarios without DR. Furthermore, the optimization of DR for a typical 4-zone household can be achieved within 15 min on an i7-12700 CPU.

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Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge Regression

Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Manel Marweni Majdi Mansouri Mansour Hajji Mohamed Faouzi Mimouni Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The modern energy landscape is undergoing a seismic change from traditional, finite energy sources and toward cleaner, renewable alternatives. The restrictions faced by traditional sources, which are not only finite but increasingly shrink in the face of burgeoning global energy demands driven by population increase and industrial expansion, are driving this change. Although promising, renewable energy poses complications, particularly the reliance on climatic conditions. An important aspect of addressing these difficulties is effective energy management within distribution systems, which includes forecasting and optimization phases. This research focuses on forecasting using an advanced machine learning (ML) approach. Accurately forecasting renewable energy generation over time is critical for improving energy management. This technique is evaluated using a variety of performance indicators, including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R 2 ). Empirical studies support the method's usefulness, demonstrating noteworthy performance with low error rates.

关键词： Photovoltaic systems Renewable energy sources Technological innovation Accuracy Machine learning Predictive models Forecasting Kernel Energy management Optimization

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Corrigendum to “First-principles calculations of phase transition, phonon spectra, thermodynamic properties and elasticity for PuO 2 polymorphs” [J. Solid State Chem. 341 (2025) 125080]

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Journal of Solid State Chemistry 2025年 342卷

作者： Junjie Ai Tengfei Ma Hang You Minghong Xie Xiaofeng Tian The College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu 610059 China Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province Chengdu University of Technology Chengdu China

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Estimation of Gaussian overlapping nuclear pulse parameters based on a deep learning LSTM model

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nuclear Science and Techniques 2019年第11期30卷 83-94页

作者： Xing-Ke Ma Hong-Quan Huang Qian-Cheng Wang Jing Zhao Fei Yang Kai-Ming Jiang Wei-Cheng Ding Wei Zhou College of Nuclear Technology and Automation Engineering Chengdu University of Technology

A long short-term memory(LSTM)neural network has excellent learning ability applicable to time series of nuclear pulse *** can accurately estimate parameters associated with amplitude,time,and so on,in digitally shaped nuclear pulse signals—especially signals from overlapping *** learning the mapping relationship between Gaussian overlapping pulses after digital shaping and exponential pulses before shaping,the shaping parameters of the overlapping exponential nuclear pulses can be estimated using the LSTM ***,the Gaussian overlapping nuclear pulse(ONP)parameters which need to be estimated received Gaussian digital shaping treatment,after superposition by multiple exponential nuclear ***,a dataset containing multiple samples was produced,each containing a sequence of sample values from Gaussian ONP,after digital shaping,and a set of shaping parameters from exponential pulses before digital ***,the Training Set in the dataset was used to train the LSTM *** these datasets,the values sampled from the Gaussian ONP were used as the input data for the LSTM model,and the pulse parameters estimated by the current LSTM model were calculated by forward ***,the loss function was used to calculate the loss value between the network-estimated pulse parameters and the actual pulse ***,a gradient-based optimization algorithm was applied,to feedback the loss value and the gradient of the loss function to the neural network,to update the weight of the LSTM model,thereby achieving the purpose of training the ***,the sampled value of the Gaussian ONP for which the shaping parameters needed to be estimated was used as the input data for the LSTM *** this,the LSTM model produced the required nuclear pulse parameter *** summary,experimental results showed that the proposed method overcame the defect of local convergence encountered in traditional methods and could accurately extract para

关键词： nuclear pulses S–K digital shaping Deep learning LSTM

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Fracture morphology of fine-grained nuclear graphite under dynamic load

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Journal of nuclear Materials 2025年 614卷

作者： Zhou, Yan Tian, Dongqing Zhou, Junjie Xu, Ke Cao, Xinlei Chen, Hui Liu, Guangyan Shi, Li Sun, Libin Shen, Ke College of Materials Science and Engineering Hunan University Hunan Changsha410082 China Hunan Province Key Laboratory for Advanced Carbon Materials and Applied Technology Hunan University Hunan Changsha410082 China Institute of Nuclear and New Energy Technology Advanced Nuclear Energy Technology Cooperation Innovation Center The Key Laboratory of Advanced Nuclear Engineering and Safety Ministry of Education Beijing 10084 China Northern Regional Office of Nuclear and Radiation Safety Inspection Beijing 100029 China School of Aerospace Engineering Beijing Institute of Technology Beijing 100081 China

nuclear graphite is widely used in high-temperature gas-cooled reactors due to its excellent neutron moderation capabilities and mechanical stability at elevated temperatures. However, graphite components in these reactors are exposed to complex environments and diverse loads, including dynamic forces potentially arising from earthquakes, aircraft impacts, and internal explosions. This study investigates the fracture behavior of nuclear-grade graphite, specifically SIAMC fine-grained graphite and IG-11 graphite, under dynamic loading by examining their fracture morphology. Fracture surface roughness of graphite discs was measured, and three-dimensional surface reconstructions were conducted. Results show a significant decrease in surface roughness with increasing strain rates, indicating a trend toward a flatter fracture surface driven by changes in crack propagation paths. Furthermore, the microscopic crack growth mechanism of fine-grained nuclear graphite under dynamic loading is elucidated, enhancing our understanding of its fracture behavior under dynamic conditions. © 2025

关键词： Aerodynamic loads

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CONFORMAL UNCERTAINTY INDICATOR FOR CONTINUAL TEST-TIME ADAPTATION

arXiv

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arXiv 2025年

作者： Lyu, Fan Zhao, Hanyu Shi, Ziqi Liu, Ye Hu, Fuyuan Zhang, Zhang Wang, Liang New Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China China Nuclear Power Engineering Co. LTD China College of Intelligence and Computing Tianjin University China School of Computer Science & Engineering LinYi University China School of Electric & Information Engineering Suzhou University of Science and Technology China

Continual Test-Time Adaptation (CTTA) aims to adapt models to sequentially changing domains during testing, relying on pseudo-labels for self-adaptation. However, incorrect pseudo-labels can accumulate, leading to performance degradation. To address this, we propose a Conformal Uncertainty Indicator (CUI) for CTTA, leveraging Conformal Prediction (CP) to generate prediction sets that include the true label with a specified coverage probability. Since domain shifts can lower the coverage than expected, making CP unreliable, we dynamically compensate for the coverage by measuring both domain and data differences. Reliable pseudo-labels from CP are then selectively utilized to enhance adaptation. Experiments confirm that CUI effectively estimates uncertainty and improves adaptation performance across various existing CTTA methods. © 2025, CC BY.

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Effect of TiO2 nanoparticles and hydrogen on the combustion, performance, and emissions of madhuca biodiesel in a diesel engine

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International Journal of Hydrogen Energy 2025年

作者： Madhan Kumar, A. Senthil Nathan, K. Manickam, Selvaraj Gupta, Deepak Vikneswaran, M. Dhamodaran, Gopinath Seetharaman, Sathyanarayanan Thiagarajan, S. Al-Ansari, Mysoon M. Department of Mechanical Engineering Saveetha Engineering College Saveetha Nagar Thandalam Chennai India Department of Mechanical Engineering E.G.S. Pillay Engineering College Tamilnadu Nagapattinam611002 India Department of Automation and Robotics IMPACT College of Engineering and Applied Sciences Bangalore India Department of Mechanical Engineering Graphic Era Hill University Uttarakhand Dehradun248002 India Department of Mechanical Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India Department of Mechanical Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram Tamilnadu Chennai600089 India Department of Mechanical Engineering Easwari Engineering College Tamilnadu Chennai India Department of Chemistry College of Science King Saud University P.O. Box. 2455 Riyadh11451 Saudi Arabia Centre for Sustainable Energy Technologies Easwari Engineering College Tamilnadu Chennai India Centre of Research SRM TRP Engineering College TiruchirapalliTamilnadu India

This study investigates the combustion, performance, and emission characteristics of a diesel engine fueled with neat diesel, M20 (a blend of 20 % Madhuca oil and 80 % diesel), and M20 augmented with Titanium dioxide (TiO2) nanoparticles (50 ppm and 100 ppm), under dual-fuel conditions with a constant hydrogen flow rate of 10 LPM. The research aims to address the challenges of biodiesel adoption by leveraging TiO2 nanoparticles and hydrogen to optimize fuel efficiency and emissions. The results reveal that the M20 blend enhanced with 100 ppm TiO2 nanoparticles achieved a peak improvement in brake thermal efficiency (BTE) by 6.76 %, while simultaneously reducing brake-specific fuel consumption (BSFC) by 4.46 %, compared to the M20 blend. Also, emission analysis demonstrated significant reductions in hydrocarbon (HC) and carbon monoxide (CO) emissions, achieving decreases of 2.63 % and 8.53 %, respectively, with the same blend. The inclusion of hydrogen as a secondary fuel with M20 and 100 ppm TiO2 showed 24.23 % improved BTE and 8.32 % reduced BSFC, along with significant emissions reductions: 13.16 % HC, 19.02 % CO, and 4.04 % smoke opacity. However, nitrogen oxide (NOx) emissions increased by 12.67 % due to elevated in-cylinder temperatures with the M20 blend. These findings underscore the potential of Madhuca oil-based biodiesel, when enhanced with nanoparticles and hydrogen, to serve as a sustainable alternative to conventional diesel. By balancing improved performance and reduced environmental impact, this study offers valuable insights into achieving cleaner and more efficient combustion in diesel engines, contributing to the advancement of biofuel applications and emission compliance strategies. © 2025

关键词： Diesel engines

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Artificial neural network-based method for discriminating Compton scattering events in high-purity germaniumγ-ray spectrometer

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nuclear Science and Techniques 2024年第2期35卷 64-84页

作者： Chun-Di Fan Guo-Qiang Zeng Hao-Wen Deng Lei Yan Jian Yang Chuan-Hao Hu Song Qing Yang Hou College of Nuclear Technology and Automation Engineering Chengdu University of TechnologyChengdu 610059China

To detect radioactive substances with low activity levels,an anticoincidence detector and a high-purity germanium(HPGe)detector are typically used simultaneously to suppress Compton scattering background,thereby resulting in an extremely low detection limit and improving the measurement ***,the complex and expensive hardware required does not facilitate the application or promotion of this ***,a method is proposed in this study to discriminate the digital waveform of pulse signals output using an HPGe detector,whereby Compton scattering background is suppressed and a low minimum detectable activity(MDA)is achieved without using an expensive and complex anticoincidence detector and *** electric-field-strength and energy-deposition distributions of the detector are simulated to determine the relationship between pulse shape and energy-deposition location,as well as the characteristics of energy-deposition distributions for fulland partial-energy deposition *** relationship is used to develop a pulse-shape-discrimination algorithm based on an artificial neural network for pulse-feature *** accurately determine the relationship between the deposited energy of gamma(γ)rays in the detector and the deposition location,we extract four shape parameters from the pulse signals output by the *** learning is used to input the four shape parameters into the ***,the pulse signals are identified and classified to discriminate between partial-and full-energy deposition *** partial-energy deposition events are removed to suppress Compton *** proposed method effectively decreases the MDA of an HPGeγ-energy dispersive *** results show that the Compton suppression factors for energy spectra obtained from measurements on ^(152)Eu,^(137)Cs,and ^(60)Co radioactive sources are 1.13(344 keV),1.11(662 keV),and 1.08(1332 keV),respectively,and that the corresponding MDAs are 1.4%,5.

关键词： High-purity germaniumγ-ray spectrometer Pulse-shape discrimination Compton scattering Artificial neural network Minimum detectable activity

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Fast nuclide identification based on a sequential Bayesian method

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nuclear Science and Techniques 2021年第12期32卷 116-127页

作者： Xiao-Zhe Li Qing-Xian Zhang He-Yi Tan Zhi-Qiang Cheng Liang-Quan Ge Guo-Qiang Zeng Wan-Chang Lai Chengdu University of Technology The College of Nuclear Technology and Automation EngineeringChengdu 610059China

The rapid identification of radioactive substances in public areas is ***,traditional nuclide identification methods only consider information regarding the full energy peaks of the gamma-ray spectrum and require long recording times,which lead to long response *** this paper,a novel identification method using the event mode sequence(EMS)information of target radionuclides is *** EMS of a target radionuclide and natural background radiation were established as two different probabilistic models and a decision function based on Bayesian inference and sequential testing was *** proposed detection scheme individually processes each *** a photon is detected and accepted,the corresponding posterior probability distribution parameters are estimated using Bayesian inference and the decision function is ***,value of the decision function is compared to preset detection thresholds to obtain a detection ***ments on different target radionuclides(137Cs and 60Co)were *** count rates of the regions of interest(ROI)in the backgrounds between[651,671],[1154,1186],and[1310,1350]keV were 2.35,5.14,and 0.57 CPS,*** experimental results demonstrate that the average detection time was 6.0 s for 60Co(with an activity of 80400 Bq)at a distance of 60 cm from the *** average detection time was 7 s for 137Cs(with an activity of 131000 Bq)at a distance of 90 cm from the *** results demonstrate that the proposed method can detect radioactive substances with low activity.

关键词： Natural radiation Nuclide identification Sequential testing nuclear safety

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Effect of unequal injection rates on asymmetric exclusion processes with junction

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Chinese Physics B 2011年第6期20卷 131-138页

作者：肖松刘明哲王建军王华 Faculty of Metallurgical and Energy Engineering Kunming University of Science and Technology College of Nuclear Technology and Automation Engineering Chengdu University of Technology

In this paper, we investigate the effect of unequal injection rates on totally asymmetric simple exclusion processes （TASEPs） with a 2-input 1-output junction and parallel update. A mean-field approach is developed to deal with the junction that connects two sub-chains and the single main chain. We obtain the stationary particle currents, density profiles and phase diagrams. Interestingly, we find that the number of stationary-state phases is changeable depending on the value of a1 （a1 is the injection rate on the first sub-chain）. When a1 〉 1/3, there are seven stationary-state phases in the system, however when a1 〈 1/3, only six stationary-state phases exist in the system. The theoretical calculations are shown to be in agreement with Monte Carlo simulations.

关键词： totally asymmetric simple exclusion process mean-field approach Monte Carlo simulations junction

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