Designing adaptive smart buildings: an RNN and Migrating Birds Optimization approach for occupancy prediction

Within the dynamic field of modern architecture and infrastructure, the concept of intelligent building adaptation arises as a fundamental element in attaining both energy efficiency and the highest level of comfort for occupants. The difficult task of occupancy prediction is crucial to the overall adaptability. The present study presents a unique methodology that integrates the computational capabilities of Recurrent Neural Networks (RNNs) with the optimization functionalities of the Migrating Birds Optimization (MBO) algorithm. The findings of our study reveal that implementing this synergistic methodology not only improves the accuracy of predictions, but also provides a more comprehensive comprehension of the dynamics of occupancy. The results of this study have important implications for the management of intelligent buildings. They indicate that the strategic combination of machine learning and optimization methods can significantly enhance the performance of adaptive building systems, promoting sustainability, and improving occupant satisfaction.