Machine learning algorithms have proven to be practical in a wide range of applications. Many studies have been conducted on the operational energy consumption and thermal comfort of radiant floor systems. This paper conducts a case study in a self-designed experimental setup that combines radiant floor and fan coil cooling (RFCFC) and develops a data monitoring system as a source of historical operational data. Seven machine learning algorithms (extreme learning machine (ELM), convolutional neural network (CNN), genetic algorithm-back propagation (GA-BP), radial basis function (RBF), random forest (RF), support vector machine (SVM), and long short-term memory (LSTM)) were employed to predict the behavior of the RFCFC system. Corresponding prediction models were then developed to evaluate operative temperature (T_op) and energy consumption (E_h). The performance of the model was evaluated using five error metrics. The obtained results showed that the RF model had very high performance in predicting T_op and E_h, with high correlation coefficients (>0.9915) and low error metrics. Compared with other models, it also demonstrated high accuracy in E_h prediction, yielding maximum reductions of 68.1, 82.4, and 43.2% in the mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE), respectively. A sensitivity ranking algorithm analysis was also conducted. The obtained results demonstrated the importance of adjusting parameters, such as the radiant floor supply water temperature, to enhance the indoor comfort. This study provides a novel and effective method for evaluating the energy efficiency and thermal comfort of radiant cooling systems. It also provides insights for optimizing the efficiency and thermal comfort of RFCFC systems, and lays a theoretical foundation for future studies integrating machine learning algorithms in this field.

Human behaviors that greatly influence building energy consumption are stimulated by the indoor environment. However, the relative importance of different environmental factors remains unclear. Previous literature mostly focused on single behavior. Holistic study of multiple energy-related behaviors is scarce. To fill the gap, this study investigated 22 government office buildings in Sichuan using questionnaires and field measurement. Environmental factors were ranked based on the two dimensions of “importance level’level” and “satisfaction level”. The key energy-related behaviors were identified by the comparative study between low- and high-energy-consuming buildings. Lastly, interactions between the building energy consumption, indoor environment quality, occupants’ satisfaction, and human behaviors were analyzed. Questionnaires reveal that most occupants consider indoor air quality as the prior “pain point” while feeling satisfied enough with the thermal environment. Although people attach less importance to the acoustic environment, they manifest evident discontent, suggesting that noise control is an urgent imperative. In contrast, occupants are relatively unconcerned with illuminance, which implies the feasibility of saving energy by reasonably reducing lighting requirements of some non-critical areas. The comparative study indicates that increased energy consumption was attributable to extra personal appliances, wasteful air conditioning habits, and the lack of ventilation in summer. The objective environment of high-energy-consuming buildings is slightly better. However, the difference in perceived satisfaction was not obvious. The findings of this study contribute to determining the most noteworthy environmental factor and the key energy-related behaviors and provide reference information for optimizing energy-saving strategies.

A chilled water system transports cooling functionality from refrigerators to users via heating, ventilation, and air conditioning (HVAC) systems. This paper investigated an optimal control strategy to regulate the volume flow rate of each user branch in a chilled water system, considering the minimum resistance operation to reduce energy consumption. An artificial neural network (ANN) was adopted to establish the nonlinear relationship between the volume flow rate of each user branch, pump frequency, and valve opening of each user branch. An optimal control strategy for a chilled water HVAC system is proposed in this article, according to the pump-valve combined control (PVCC) principle and an ANN model, i.e., pump-valve combined control using an artificial neural network model (PVCC-ANN). A series of tests were conducted to collect data to train the ANN model and analyze the performance of the PVCC-ANN in an experimental chilled water system. The results show that the trained ANN model has good prediction performance. A minimum resistance operation can be achieved to control the volume flow rate of each user branch independently by using the PVCC-ANN model. Moreover, the proposed PVCC-ANN method shows good energy-saving performance in chilled water systems, which can be attributed to the minimum resistance operation. Taking the energy consumption of the pump’s constant frequency operation as the reference, the energy saving rate using the PVCC-ANN is between 14.3% and 58.6% under 10 operating conditions, as reported in this paper.

Energy shortage and environmental pollution have accelerated the adoption of lithium-ion batteries (LIBs) as efficient energy storage solutions. However, their performance and safety challenges under extreme temperatures highlight the urgent need for effective temperature control during charging and discharging, making a comprehensive understanding of electrochemical and thermal behaviors crucial. This paper develops a 3D electrochemical–thermal coupled model for 150 Ah lithium iron phosphate (LFP) batteries to investigate thermal behavior at varying charge–discharge rates. An integrated learning regression prediction system, incorporating a structured vector autoregression (SVAR) model, is subsequently proposed to analyze the interactions among multiple electrochemical and thermal variables. The temperature difference exceeds 5 °C at higher charging rates (1.3C, 1.5C), driven primarily by accelerated heat generation—especially reversible heat. Complex interactions exist between electrochemical and thermal parameters. When charging at 0.5C, voltage, current density, battery capacity, and the maximum temperature difference (MTD) are all significantly and positively correlated (p < 0.001). Under 1C discharge conditions, voltage exhibits a strong positive correlation with most thermal characteristic variables, and correlation coefficients across different operating conditions range from −0.9731 to 0.973. Finally, the proposed ensemble learning system exhibits excellent prediction accuracy, strong generalization, and robust trend analysis, with practical guiding value.

Background: This paper proposes a defect identification method for vehicle-mounted cable terminals in electric multiple units (EMUs) based on the improved Graham angle field and residual network to address the issue of low recognition accuracy caused by the lack of partial discharge (PD) and identification data for Ethylene Propylene Rubber (EPR) cable terminal defects. Methods: The improved Gramian angular field (IGAF) characteristic transformation method was used to transform the PD one-dimensional time-series signal into a two-dimensional one after cable terminals with four common insulation defects were constructed, and a PD detection platform was built. Finally, an anti-aliasing downsampling module and attention mechanism were added to the residual network ResNet101 model. The Center loss and Softmax loss functions were integrated to increase accuracy for training and recognition classification. Topological feature images improved the distinguishability of defect categories. Results: The test results showed that the diagnostic method has an accuracy rate of 97.3% for identifying PD at the cable terminal. Conclusion: The proposed diagnosis model has higher recognition accuracy and better balance than other conventional fault diagnosis methods, making it suitable for diagnosing high-voltage cable faults in EMU trains.