The time deviation of building energy consumption data and its synchronization based on the CCKF-SPI-EP framework

• Time deviations are found in building electricity consumption monitoring data. • Hourly energy usage data is most affected with up to ± 20% numerical deviations. • A practical suggestion is put forward to improve the data acquisition protocol. • The CCKF-SPI-EP method is proposed to synchronize temporally deviated time series. • The proposed method outperforms existing methods with consistency on all test subjects. Building energy consumption data quality plays a critical role in analytical accuracy, yet temporal accuracy remains underexplored compared to numerical accuracy in existing research. Our analysis of monitoring platform data reveals temporal deviations inducing up to ± 20% numerical deviations of hourly data in extreme circumstances. To address this, we develop the CCKF-SPI-EP methodology, a novel multi-sensor data fusion framework that achieves simultaneous time synchronization and constraint optimization through three key techniques: a Constrained Centralized Kalman Filter framework with normalization of cumulative energy sequences, a Shape-Preserving Interpolation for monotonic time registration, and an Estimation Projection technique for constraint incorporation. Experimental results demonstrate the method’s superiority with more than 42%-67% reduction in RMSE and 59%-76% reduction in MAE on the building’s main meter compared to the best conventional method. Furthermore, we provide practical recommendations for improving data acquisition protocols to incorporate temporal accuracy into building energy data quality assessment. This work not only presents an effective correction framework but also makes forward-looking contributions in problem awareness and data quality system development for building energy informatics.