Abstract
Forecasting room utilization based on indoor environmental conditions offers a novel approach, which improves energy efficiency and also delivers the personalized indoor comfort. This study investigates whether parameters such as CO(2) concentration, illumination, humidity, and temperature which can reliably predict the room occupancy. This work introduces a new deep learning-augmented predictive energy modeling (DL-PEM) framework combined with multi-objective particle swarm optimization (MOPSO) for real-time, occupancy-based energy management in intelligent buildings. In contrast to conventional linear predictive or rule-based systems, DL-PEM utilizes a deep feedforward neural network (DNN) that can extract non-linear relationships between indoor environmental variables (CO(2), lighting, humidity, temperature) and occupation patterns. Using a Pareto-optimal approach to manage trade-offs, this real-time system minimizes energy consumption (kWh), reduces CO2 concentration (ppm), and maximizes the occupant thermal comfort index. It does this by adjusting adaptive HVAC and lighting control via MOPSO. The combined framework illustrates enhanced adaptability under changing conditions and scalability towards wider deployment. Experimental results indicate that the method proposed here attains 99.8% accuracy and at most 85% optimization efficiency, outperforming KNN, DT, AO-ANN, and even LSTM baselines for prediction and control tasks. Empirical evaluation using real building data demonstrates that the proposed DL-PEM-MOPSO framework significantly outperforms traditional models, enhances decision-making transparency, and offers a scalable, future-ready solution for smart building energy management. It improves occupancy data analysis and adapts to seasonal fluctuations, which also optimizes the thermal comfort and enables the accurate power demand forecasting, and enhances overall energy utilization.