At present, the traditional control strategy of heating systems is still unable to achieve building heating on demand, which enhances the energy consumption of heating and affects the thermal comfort of buildings. Therefore, this study puts forward a novel data-driven MPC for building thermal inlet, which allows the optimal operation of the district heating system and has been verified by simulation with three public buildings. In this method, the indoor temperature at the next moment reaches the temperature set value by changing the current flow rate. First, based on the energy consumption monitoring platform and the measured data of the buildings, the building indoor temperature prediction model at the next moment is established by using long short-term memory (LSTM). Compared with subspace model identification (SMI), LSTM has higher prediction accuracy, and the R² was about 0.9 in three buildings. Second, the particle generated by particle swarm optimization, which represents the flow variation, is input to the trained LSTM to predict the indoor temperature. By minimizing the objective function, the optimal flow change at the current time can be calculated. The results showed that the MPC based on a data-driven model can adjust the flow rate in time to maintain a stable indoor temperature with ±0.5 °C error. In addition, when the temperature setting needs to be changed, the indoor temperature can reach the new set value in 3 h, which outperforms the PID control. The method proposed in this paper can greatly reduce the influence of regulation lag by adjusting the flow in advance.

Road tunnels have recently become prevalent in urban areas of China. In areas adjacent to these tunnels, air quality and human health are important public health concerns. As such, assessing pollutant dispersion and decay rates around tunnel portals in the surrounding environment is an important field of study. This study established a three-dimensional computational fluid dynamics (CFD) model containing two adjacent urban road tunnels, to estimate pollution dispersion characteristics outside the tunnels. The results indicate that, when pollutants were exhausted from the portals only, crossflow between the two adjacent tunnels was quite severe. The highest carbon monoxide (CO) concentration was nearly 50 ppm, exceeding the 10 ppm limit National Standard in China for 1 h exposure when exhaust shafts are absent. When shafts exhausted 80% of the pollutants from the tunnel portal, crossflow between the adjacent tunnels decreased to 10-50% of that without shafts. The simulation results demonstrate that the combination of shafts and portals was an effective and energy-saving strategy to exhaust pollution from adjacent super-long urban road tunnels. These findings increase our fundamental understanding and provide guidelines for evaluating pollutant exposure risk around tunnel portals, the design of tunnel exhaust shafts, and environmental planning purposes in urban environments.

Among sub-items of energy consumption in public buildings, lighting sockets play an important role in energy-saving analysis. So, the energy consumption data quality of lighting sockets is important. However, limited by the initial cost of energy monitoring platform, it is difficult to install electricity meters covering all branches and to retrofit the incompact classification electricity branches, which results in a mixture of the lighting socket energy consumption and other components. In this study, a separation methodology is proposed. First, the abnormal data in the energy monitoring platform are cleaned and screened using a clustering algorithm. Second, the average outdoor air temperature partitioning model (OATPM) method and the k-nearest neighbor (KNN) clustering algorithm method are proposed for identifying and separating the abnormal data. These two methods have complementary advantages in the best applicable scenarios, including calculation accuracy and other aspects. The verification results for three buildings show that the relative error of this separation methodology is less than 15%. Finally, this paper presents the optimization parameters of the KNN method. Through this methodology, building managers need only historical data in an energy monitoring platform to separate the combined power consumption of the lighting sockets and air-conditioning online, independent of detailed information statistics.

The extraction and analysis of electricity consumption changing patterns are increasingly important, as they can guide in energy management and efficiency retrofitting. Consequently, it is necessary to extract the laws governing building electricity consumption characteristics. This method should be on an hour-scale and successfully applied online to various buildings. Under these conditions, the method should be as simple as possible to ensure excellent online applications. A matrix model method was developed based on the conventional K-nearest neighbour clustering algorithm, which introduced the concept of electricity intensity levels. This method used the slopes of the power consumption curve as the grading standard for the extraction and assessment of the electricity consumption laws. The validation results for seven different buildings with various functions and climate zones, including the mean absolute error, mean absolute percentage error, mean square error, root mean square error, and coefficient of variance, showed that this method met the aforementioned requirements. Moreover, this method can be used for power consumption prediction, which integrated a process of filtering historical data, leading to better accuracy and less data volume than that of other methods that use historical data for prediction. Practical application: This paper proposed a matrix model method based on the conventional K-nearest-neighbour clustering algorithm, which introduced the concept of electricity intensity levels. This method was applied to various buildings online, which coupled the process of filtering historical data and flexible selectivity of models when used on different buildings. This method was used for assessing energy-saving potential, energy-saving retrofit priorities, and power consumption forecasting, which will benefit researchers and engineers.