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The data-driven models have been widely used in building energy analysis due to their outstanding performance. The input variables of the data-driven models are crucial for their predictive performance. Therefore, it is meaningful to explore the input variables that can improve the predictive performance, especially in the context of the global energy crisis. In this study, an algorithm for calculating the balance point temperature was proposed for an apartment community in Xiamen, China. It was found that the balance point temperature label (BPT label) can significantly improve the daily energy consumption prediction accuracy of five data-driven models (BPNN, SVR, RF, LASSO, and KNN). Feature importance analysis showed that the importance of the BPT label accounts for 25%. Among all input variables, the daily minimum temperature is the decisive factor that affects energy consumption, while the daily maximum temperature has little impact. In addition, this study also provides recommendations for selecting these model tools under different data conditions: when the input variable data is insufficient, KNN has the best predictive performance, while BPNN is the best model when the input data is sufficient.

Reliable energy consumption forecasting is essential for building energy efficiency improvement. Regression models are simple and effective for data analysis, but their practical applications are limited by the low prediction accuracy under ever-changing building operation conditions. To address this challenge, a Joinpoint–Multiple Linear Regression (JP–MLR) model is proposed in this study, based on the investigation of the daily electricity usage data of 8 apartment complexes located within a university in Xiamen, China. The univariate model is first built using the Joinpoint Regression (JPR) method, and then the remaining residuals are evaluated using the Multiple Linear Regression (MLR) method. The model contains six explanatory variables, three of which are continuous (mean outdoor air temperature, mean relative humidity, and temperature amplitude) and three of which are categorical (gender, holiday index, and sunny day index). The performance of the JP–MLR model is compared to that of the other four data-driven algorithm models: JPR, MLR, Back Propagation (BP) neural network, and Random Forest (RF). The JP–MLR model, which has an R2 value of 95.77%, has superior prediction performance when compared to the traditional regression-based JPR model and MLR model. It also performs better than the machine learning-based BP model and is identical to that of the RF model. This demonstrates that the JP–MLR model has satisfactory prediction performance and offers building operators an effective prediction tool. The proposed research method also provides also serves as a reference for electricity consumption analysis in other types of buildings.

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In this paper, a medium-frequency inverter spot welder was used for resistance spot-welding experiments on 980 MPa grade cold-rolled δ-TRIP(Transformation-induced Plasticity) steel. The effects of the tempering process on the morphology, microstructure, element distribution, and properties of spot-welded joints were studied by Scanning Electron Microscope (SEM), Transmission Electron Microscopy (TEM), and Electron-Probe MicroAnalysis (EPMA). The microstructure of the nugget zone obtained by single-pulse process was δ ferrite, lath martensite, and twin martensite. After adding tempering under the single-pulse process, the microstructure was δ ferrite and lath martensite. However, the morphology of the microstructure was still dendritic, which remained unchanged. The tensile shear failure of spot-welded joints under the two processes was an interface failure, and the fractures were cleavage fractures. After adding tempering, the interface fracture surface presents two kinds of fracture characteristics: the outer cracks’ growth direction was consistent with the columnar crystal growth direction, and the inner crystal cracks occurred in the nugget core and finally grew along the columnar grain boundary. Due to the significant hardness difference between δ ferrite (283 HV) and martensite (533 HV), the low-strength δ ferrite phase was the main channel of crack propagation. After adding tempering, the hardness distribution of the spot-welded joints was more uniform and the tensile shear force increased (7.4 kN→8.5 kN).