An adaptive uncertainty-aware hybrid neural network for enhanced learning in real-time building energy prediction with dynamic occupant behavior modeling

Abstract

Precise real-time prediction of energy consumption in buildings is very essential to smart energy management, but is extremely challenging because of the dynamic and unpredictable behavior of occupants. These variations are not typically represented in traditional statistical and machine learning models, leading to poorer forecasting performance and reduced reliability in practical applications. We aim to fill this gap by introducing a new framework called AU-HNN (Adaptive Uncertainty-Aware Hybrid Neural Network), which thoroughly combines multi-scale hybrid deep learning (CNN, LSTM, Transformer) with Bayesian uncertainty quantification and dynamic occupant behaviour modeling. The model features online incremental learning to conform to changing behavioral patterns and to give probabilistic estimates with calibrated confidence intervals. Extensive experiments were performed on real-world data sets of the eight major cities in China to compare AI-HNN to ten state-of-the-art models, such as ARIMA, Prophet, XGBoost, Random Forest, CNN-LSTM, BiLSTM, Transformer, GRU, Bayesian-LSTM, and MC-Dropout CNN. Findings reveal that AU-HNN can improve 12 performance measures by 1720 percent with significant accuracy (RMSE = 7.42, MAE = 5.58, R² = 0.923, NRMSE = 0.137) and uncertainty quantification (PICP = 0.948, PINAW = 0.227, CWC = 14.2). Moreover, AU-HNN demonstrates competitive real-time (latency = 189 ms, memory usage = 142 MB, energy efficiency = 9.810⁶ FLOPS/Watt) and can be deployed in the smart edges. The proposed framework offers extremely precise, adaptive, and uncertainty-sensitive energy predictions to support risk-based decision-making by building operators and energy managers. Its ability to capture the human-environment dynamic encapsulated in occupant dynamics and energy usage creates a very new path to smarter, more resilient, and sustainable building energy management systems.

Keywords

Building energy prediction hybrid neural networks uncertainty quantification occupant behavior modeling adaptive learning real-time forecasting

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