ATM-SB: An indoor temperature prediction approach for smart buildings based on deep learning

In recent years, machine learning (ML) and deep learning (DL) have emerged as key technologies for balancing energy efficiency and thermal comfort in Internet of Things (IoT) based Smart Buildings (SBs). Among the various components of SBs, the heating, ventilation, and air conditioning (HVAC) system plays a critical role, as it significantly influences both energy consumption and occupant comfort. In this context, accurately predicting indoor temperatures is essential for optimizing HVAC operations, resulting in enhanced energy efficiency, improved comfort, and lower energy costs. To address this challenge, this paper proposes Advanced Temperature Management of Smart Building (ATM-SB), a hybrid DL approach that combines Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks to predict indoor temperature at 15, 30, and 60 minute intervals using multivariate sensor data. Using the ATM-SB approach, a prototype was developed at AEI S.r.l., Italy. It was trained and validated on a real-world dataset collected from a smart office building and laboratory across both summer and winter seasons, and further tested on four public datasets from diverse environments. ATM-SB achieves competitive performance and demonstrates robust generalization across diverse real world and public datasets, including comparisons with LSTM, GRU, CNN-LSTM, and gradient boosting, achieving an MAE as low as 0.0098 and an R 2 of up to 0.98 on real data. Statistical validation through 5-fold cross-validation and significance testing confirms the robustness and generalization of the model. By enabling accurate forecasting across diverse scenarios, ATM-SB provides an effective solution for intelligent HVAC control in next-generation SBs.