China’s “dual carbon” goals, energy conservation and emission reduction in the energy system, have become increasingly important. The sensor fault of an energy system will cause unstable operation and increase energy consumption. Therefore, this study proposes a new sensor fault detection strategy based on the data driven method for energy saving and emission reduction. However, for data-driven models, data quality has a greater impact on model performance. This study innovatively uses five machine learning methods to optimize the energy system operating data. Five machine learning methods include the moving average (MA), Lowess, Loess, Rlowess and Rloess methods. Fault detection performances of different data driven models optimized by different approaches are compared and analyzed. Besides, data outliers and parameter selection of data optimization methods are discussed. The results indicate that the MA method has the best optimization performance when the smoothness degree is level 2. The optimized data fluctuation range is controlled within the range of ±1. The fault detection accuracy rate of the model optimized based on the MA method is increased from 32.51% to 83.96% when the evaporation temperature sensor fault is a 5 °C deviation. However, the data will deviate from the original data trend when the smoothing parameter is set too large. Therefore, the smoothness of the data should not be too large. The approach proposed in this study is of great significance to the energy saving and emission reduction of the energy system.

Underground commercial buildings have received increasing attention as an emerging place of consumption. However, previous studies on underground commercial buildings have mainly focused on the impact of a specific environment on comfort or energy consumption. Few studies have been conducted from the perspective of functional use. The purpose of this paper is to investigate, in terms of functional angles, the indoor thermal environment and air quality of an underground commercial building in Zhengzhou, China, and put forward an optimal control strategy of ventilation organization. The results showed that the relative humidity of the underground shopping mall was generally above 60%, and the average temperature of 29.1 °C led to a thermal comfort problem in the catering area in summer. Meanwhile, the concentration of CO₂ exceeded the allowed figures during the peak of the customer flow rate, and PM_2.5 concentration in the catering area also exceeded the standard, by 43.3% and 33.3%, respectively. Furthermore, to solve the indoor thermal environment and air quality problems found in the field measurements, this study assessed the air distribution by adopting three different air supply schemes for the catering area. Optimization results showed that compared with the ceiling supply, the side supply scheme kept the air temperature 0.4 °C cooler in summer and 0.5 °C warmer in winter. The temperature uniformity increased by 5.4% and 3.7%, and the velocity uniformity increased by 6.5% and 8.8%, respectively. This study can provide theoretical support for thermal environment construction and ventilation organization control of underground commercial buildings.

The intermittent heating mode of Kang plays an important role in the heat storage and release in cave dwellings. However, research on the effect of Kang heating on the thermal process of traditional buildings is rare. Therefore, based on long-term monitoring of cave dwellings, regular conclusions about the influence of Kang heating on the thermal environment were obtained. Furthermore, an unsteady heat transfer model of the envelope was proposed for the first time. Then, based on this model, the thermal storage performance of cave dwellings during the period of Kang intermittent heating was explored. The results showed that, due to Kang heating, the indoor air temperature of cave dwellings could be increased by an average of 3.1 °C. Furthermore, the inner walls had a large thermal mass and the maximum heat storage in a single day was 487.75 kJ/m², while the maximum heat release was 419.02 kJ/m². The heat release at night could reach 87%. In this paper, the law of thermal storage and release characteristics of earthen building envelopes under intermittent heating was firstly obtained. Results can enrich the thermal process theory of earthen buildings and provide a theoretical basis and technical support for building thermal environmental construction.

Indoor environmental quality (IEQ) plays a crucial role in human health, comfort, and productivity. Traditional monitoring systems often rely on single parameter measurements, which cannot comprehensively reflect the complex interactions among different indoor environmental factors. To address this limitation, this paper proposes a multi-sensor fusion monitoring system for indoor thermal, acoustic, and lighting environments. The proposed system adopts a distributed Internet of Things (IoT) sensing architecture combined with edge–cloud collaborative computing. Multiple sensors are deployed to collect environmental parameters, including temperature, humidity, sound pressure level, and illuminance. A hierarchical data processing framework is developed to perform data preprocessing, feature extraction, and multi-sensor fusion for comprehensive environmental evaluation. Experiments conducted in typical indoor scenarios demonstrate that the proposed method significantly improves monitoring accuracy and prediction performance compared with several baseline approaches. Experimental results show lower MAE and RMSE values and higher accuracy, indicating that the proposed system provides an effective solution for intelligent indoor environmental monitoring and management.