Recently, the integration of Internet of Things (IoT) into buildings has sparked a technological revolution known as the Internet of Building Things (IoBT). Such new paradigm connects devices, sensors, and systems for intelligent control. Particularly, IoBT has advanced energy forecasting and enabled real-time data analysis and predictive optimization for smarter and more efficient energy management. This paper proposes FETRA, a FEderated TRansformer with Attention-based client weighting and adaptive FedProx regularization for smart building energy prediction. FETRA addresses data heterogeneity through dynamic client importance assignment, captures long-term temporal dependencies via specialized attention mechanisms, and enhances convergence stability under non-IID distributions. Experimental evaluation on the ASHRAE dataset with 100 heterogeneous buildings demonstrates that the Informer model achieves superior performance with 94.18% prediction accuracy, MSE of 892.45, RMSE of 29.87, and $\mathrm{R}^{2}$ of 0.94, outperforming baseline techniques. In addition, the edge-fog-cloud architecture adapted in FETRA enables privacy-preserving distributed energy forecasting while maintaining computational efficiency. Results confirm FETRA's effectiveness in balancing prediction accuracy, system robustness, and scalability for real-world deployment in decentralized building energy management systems.
A novel transformer-based wide and deep convolutional neural network (TWiDeCNN) is proposed to efficiently identify electric energy theft in a scenario based on SGs, demonstrating its stability and effectiveness for electric energy theft detection.
Accurate prediction of chiller energy consumption is crucial for the efficient operation and intelligent management of heating, ventilation, and air conditioning (HVAC) systems in large buildings. However, such prediction remains challenging due to the multi-scale temporal coupling and nonstationary dynamics of chiller systems. Therefore, an IMVMD-MADNet hybrid framework integrating Improved Multivariate Variational Mode Decomposition (IMVMD) and a Multi-scale Aggregation Decomposition Network (MADNet) is proposed for chiller energy consumption prediction. To avoid information leakage and capture multi-scale features, a rolling local decomposition strategy with adaptive mode selection is employed. First, IMVMD performs stepwise decomposition within a sliding window, and the optimal number of modes is determined using envelope entropy. Then, sample entropy is used to reconstruct the multivariate modes into high-, medium-, and low-frequency components. Subsequently, a dual-branch MADNet combining wavelet-domain time-frequency modeling (WDP) and time-domain causal dependency modeling (TDP) predicts each component, and the results are aggregated to generate the final prediction. Bayesian optimization is employed to optimize the key hyperparameters. One year of real industrial chiller data from a plant in Huizhou, China, is used to evaluate the proposed model against 11 forecasting models. Results show that the dual-branch architecture outperforms single-branch models. The proposed model achieves the best performance across all forecasting horizons, with its advantage becoming more pronounced as the forecasting horizon increases, demonstrating stable predictive performance under the same-plant setting.
A three-layer “Aggregator-Edge-End user” architecture leveraging a data-driven workflow and a hybrid deep learning model combining a Temporal Convolutional Network (TCN) and a Bidirectional Long Short-Term Memory (BiLSTM) network is introduced.
A framework for a comparative evaluation of four representative forecasting methods: the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, Extreme Gradient Boosting, the Long Short-Term Memory (LSTM) neural network, and a hybrid Variational Mode Decomposition–LSTM (VMD-LSTM) model is proposed.
A new hierarchical forecasting structure denoted as INRBO-SSA-LSTM, which significantly outperforms traditional baseline models across all indicators and effectively accommodates temporal demand variations, offering a robust foundation for the advancement of intelligent power management technology.
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