An infiltration load calculation model of large-space buildings based on the grand canonical ensemble theory

Air infiltration significantly impacts building energy consumption and the indoor thermal environment. The proportion of infiltration load in large-space buildings is higher than in normal buildings. Due to the particularity of building structure and function, the existing building infiltration load calculation methods are unsuitable for large-space buildings. This paper proposed a new method for analyzing the building infiltration process from a thermodynamic perspective, enabling large-space buildings' infiltration load to be calculated more accurately and quickly. Compared with the existing method, it was found that a fixed proportional difference existed, and the proposed method relied on certain assumptions. The correction factor η was introduced to correct these limitations. The proposed method was verified to be accurate and universal in actual buildings. The paper also presents the results of η in different building scenarios and discusses the influence of mechanical ventilation and ideal gas assumption on η. Compared with the air change method and the gap method, the correction factor is stable in the range of 0.257–0.278 and 0.327–0.353, respectively. Furthermore, the correction factor for a monatomic ideal gas is 40% higher than that for a diatomic ideal gas. The new method provides a more accurate and efficient way to calculate the infiltration load, which can help improve the energy efficiency of buildings and the indoor thermal environment.Post-print / Final draf

Building Load Prediction Model Based on Integration of Mechanism and Data in District Heating Systems

For the building load prediction in district heating systems, the traditional methods cover the mechanism models and the data-driven model. However, the data of the district heating systems was often of low quality or partly missing, and thus the traditional models cannot present high accuracy in load prediction due to the insufficient data available for training. This paper proposed a building load prediction model based on the integration of mechanism and data in district heating systems. The proposed model considered the heat transfer mechanism process and was trained by historical data. As for the training results in the scenario of insufficient data, the proposed prediction model presented a remarkable improvement by decreasing the MAPE and RMSE to 7.34% and 7.74%, respectively, on the same test dataset, compared with the data-driven model. The mechanism process integrated into the proposed model served as an additional feature and enhanced the data quality.Post-print / Final draf

Investigation of hybrid modeling and its transferability in building load prediction used for district heating systems

In the district heating systems, the historical operation data of the buildings in those areas would be partially or entirely missing. The traditional data-driven model is hard to predict the ground truth results because the historical data is not available for model training. However, utilizing the physics-based methods for load calculation takes a long time to process and encounters low accuracy issues. This paper investigates several hybrid models that integrate the data-driven model and the physics-based models with different fusion methods. The physics-based models calculate envelope load and infiltration load, based on Fourier's law and the grand canonical ensemble theory, respectively. After undergoing load processing, features fusion, and residual connection, the best advanced hybrid models generate 21.35%, 16.35%, and 12.73% better prediction results compared with the data-driven model. Moreover, the advanced hybride models also perform strong transferability across all the data quantity groups. In terms of practical application, the advanced hybrid models could be deployed with effective generalization in limited data scenarios and robust transfer capabilities. The selected best model constructed by hybrid modeling displays the highest performance and saves the total training costs with strong transferability.Post-print / Final draf

Investigation on the Status Quo of Self-Health Management of Patients with Bipolar Disorder and Analysis of Influencing Factors

In this study, we aim to explore the status quo of self-health management of patients with bipolar disorder and influencing factors. A total of 80 patients with bipolar disorder were included and assigned into the observation group and the control group, with 40 cases per group. The patients in the control group were given general health education, and those in the observation group were given personalized education combined with peer support. It was found that there was no significant difference in gender, age, educational degree, marital status, and living state between the observation group and the control group ( P &gt; 0.05 ). There are significant differences with regard to self-management between the two groups. The observation group showed stronger self-care ability, health education level, self-protection ability, and emotional control ability than the control group ( P &lt; 0.05 ). Before the intervention, no significant difference in Hamilton Depression Rating Scale (HAMD) scores was observed between the observation group and the control group ( P &gt; 0.05 ). After the intervention, the HAMD scores were lower in the observation group than in the control group ( P &lt; 0.05 ), suggesting an alleviation in manic and depressive episodes in the observation group. It was also revealed that the observation group exhibited lower Functioning Assessment Short Test (FAST) scores in all aspects than the control group, suggesting personalized education combined with peer support could significantly improve the quality of life of patients with bipolar disorder. Self-health management ability of the patients was regarded as the dependent variable and healthy education level, health literacy, and objective support as the independent variables, and the regression model was performed. The results show that information acquisition ability, communication and interaction ability, objective social support, subjective social support, and utilization of social support can all affect the self-health management ability of patients. Personalized education combined with peer support can effectively alleviate the condition of patients with bipolar disorder, improve the degree of overall functional impairment, enhance the patient’s self-management ability, and promote their recovery. It is worthy of promotion and application. In conclusion, information acquisition ability, communication and interaction ability, objective social support, subjective social support, and utilization of social support can all affect the self-health management ability of patients.</jats:p