IoT-Enabled Data-Driven Optimization of Dynamic Thermal Loads for Low-Energy Buildings

Energy-efficient building operation requires accurate prediction and optimization of dynamic thermal loads under noisy IoT data streams. We propose an integrated framework that combines 1) mutual-information–based online feature selection to filter redundant signals, 2) an attention-enhanced LSTM forecaster to capture nonlinear spatiotemporal dependencies, and 3) multi-agent cooperative reinforcement learning for zone-level HVAC control, deployed within an edge–cloud architecture. Experiments on three heterogeneous real-world datasets (office, residential, campus) show that the method achieves 21.7% median energy savings (IQR 18.9–23.1%), improving over MADDPG by +5.8 percentage points (p=0.004, Wilcoxon). Forecasting accuracy is also improved, with MAE reduced by 16.7% (95% CI 12.4–20.1%) compared with Seq2Seq+Attention. Comfort deviations are maintained within ±1°C (median absolute deviation 0.32°C). Robustness tests indicate graceful degradation under σ≤0.2 Gaussian noise and ≤20% missing data, while ablation confirms the contribution of each module. Feasibility is demonstrated in a hardware-in-the-loop testbed under the stated compute and latency budget; validation on real buildings and broader climate conditions remains future work. This study contributes to smart building energy management, IoT-based HVAC control, and sustainable operation optimization.