Adaptive Clustering and Deep Learning-Based Model for Predicting Large-Scale Heterogeneous Building Load Profiles

Large-scale building load prediction is essential for efficient energy management in smart and low carbon cities. However, the heterogeneity of buildings and sensor-generated noise pose significant challenges to prediction accuracy. To address these challenges, this study proposes an adaptive clustering and deep learning-based model that integrates noise reduction, adaptive clustering, and advanced time-series modeling incorporating both static and dynamic information. The denoising module employs the Savitzky–Golay filter to remove data noise, the clustering module considers the AFW-K-means-based adaptive clustering to group buildings, and the prediction module utilizes the temporal fusion transformers algorithm that includes static and dynamic features. The proposed model was validated using real-world data collected from 182 buildings over three months in Shenzhen, China. Model comparisons show that the proposed model can reduce RMSE by up to 12.28% compared with traditional models. Compared with constructing individual models for each building, the proposed model improves prediction performance by up to 8.22% while reducing prediction time by 99.12%. Feature importance analysis reveals that historical load and time-domain, time-related, and environmental features are the main predictors, reflecting strong diurnal and climatic effects. These findings characterize the performance of the proposed approach for heterogeneous multi-building load forecasting within the investigated Shenzhen dataset. Its generalizability across seasons, years and geographical regions requires further validation.