This study introduces a novel approach, combining radial basis function neural network (RBFNN) and model predictive control (MPC) techniques to enhance energy efficiency in HVAC systems for sustainable buildings. The proposed methodology is evaluated in a single-story commercial and residential building in Najran, Saudi Arabia, utilizing new input parameters such as ambient temperature, cooling load, and compressor speed, alongside output metrics including room temperature and total exergy destruction and coefficient of performance (CoP) of the HVAC system. Significant improvements in energy management practices were observed, with a reduction in energy consumption by approximately 15% compared to conventional control models. The model’s predictive capabilities were validated against real-world electricity consumption data, demonstrating a high correlation with discrepancies ranging from 0.2% to 2.5%. Furthermore, the integration of machine learning techniques enabled more precise control of HVAC operations, addressing concerns regarding the system’s dynamic behavior and optimizing performance under varying occupancy patterns. While in the commercial building, the model achieves RMSE and CV values of approximately 1.0 and 0.61 for room temperature, 1.21 and 0.48 for exergy destruction, and 0.65 and 0.30 for CoP. However, for the residential building, RMSE and CV values are approximately 0.95 and 0.69 for room temperature, 1.08 and 0.31 for exergy destruction, and 0.55 and 0.27 for CoP.

While existing building energy prediction methods have advanced significantly, they face fundamental challenges in simultaneously modeling complex spatial–temporal relationships between buildings and integrating dynamic weather patterns, particularly in dense urban environments where building interactions significantly impact energy consumption patterns. This study presents an advanced deep learning system combining temporal graph neural networks with weather data parameters to enhance prediction accuracy across diverse building types through innovative spatial–temporal modeling. This approach integrates LSTM layers with graph convolutional networks, trained using energy consumption data from 150 commercial buildings over three years. The system incorporates spatial relationships through a weighted adjacency matrix considering building proximity and operational similarities, while weather parameters are integrated via a specialized neural network component. Performance evaluation examined normal operations, data gaps, and seasonal variations. The results demonstrated a 3.2% mean absolute percentage error (MAPE) for 15 min predictions and a 4.2% MAPE for 24 h forecasts. The system showed robust data recovery, maintaining 95.8% effectiveness even with 30% missing values. Seasonal analysis revealed consistent performance across weather conditions (MAPE: 3.1–3.4%). The approach achieved 33.3% better prediction accuracy compared to conventional methods, with 75% efficiency across four GPUs. These findings demonstrate the effectiveness of combining spatial relationships and weather parameters for building energy prediction, providing valuable insights for energy management systems and urban planning. The system’s performance and scalability make it particularly suitable for practical applications in smart building management and urban sustainability.

This study introduces a comprehensive framework combining building information modeling (BIM), project management body of knowledge (PMBOK), and machine learning (ML) to optimize energy efficiency and reduce environmental impacts in Riyadh’s construction sector. The suggested methodology utilizes BIM for dynamic energy simulations and design visualization, PMBOK for integrating sustainability into project-management processes, and ML for predictive modeling and real-time energy optimization. Implementing an integrated model that incorporates building-management strategies and machine learning for both commercial and residential structures can offer stakeholders a thorough solution for forecasting energy performance and environmental impact. This is particularly essential in arid climates owing to specific conditions and environmental limitations. Using a simulation-based methodology, the framework was evaluated based on two representative case studies: (i) a commercial complex and (ii) a residential building. The neural network (NN), reinforcement learning (RL), and decision tree (DT) were implemented to assess performance in energy prediction and optimization. Results demonstrated notable seasonal energy savings, particularly in spring (15% reduction for commercial buildings) and fall (13% reduction for residential buildings), driven by optimized heating, ventilation, and air conditioning (HVAC) systems, insulation strategies, and window configurations. ML models successfully predicted energy consumption and greenhouse gas (GHG) emissions, enabling targeted mitigation strategies. GHG emissions were reduced by up to 25% in commercial and 20% in residential settings. Among the models, NN achieved the highest predictive accuracy (R² = 0.95), while RL proved effective in adaptive operational control. This study highlights the synergistic potential of BIM, PMBOK, and ML in advancing green project management and sustainable construction.