A New Building Information Modeling Probabilistic Model Based On Artificial Intelligence to Optimize Residential Buildings Energy Efficiency in Jordan - Jaser Mahasneh; Tasnim Almigbel (Future Cities and Environment, 2024)
The demand for energy in Jordan’s residential buildings is increasing, resulting in significant discrepancies between predicted and actual energy usage. Accurately predicting household energy usage is crucial for sustainable building planning and effective energy management strategies. However, traditional energy models often need to pay more attention to the complexity of occupant behavior, leading to significant differences between expected and actual energy usage. To improve energy consumption forecast accuracy, a unique approach was proposed in this study, which combined Time-Use Survey (TUS) data with AI-driven algorithms in Building Information Modeling (BIM). The study conducted a time-use survey in 100 Irbid, Jordan residences to document detailed inhabitant behavior and daily activity patterns. The collected data was then used to train AI algorithms integrated into the BIM framework. This integration enables the BIM model to dynamically adapt and estimate energy usage based on real-time occupant behaviors and environmental conditions instead of relying solely on static architectural and mechanical inputs. Using this BIM model with AI significantly reduced the difference between expected and actual energy usage in the analyzed houses. The findings of this study support the usefulness of incorporating occupant behavioral data into energy prediction models. This approach provides more accurate energy consumption projections and highlights the importance of considering human aspects throughout the architectural design and energy planning stages.