iBuilding: artificial intelligence in intelligent buildings

This article presents iBuilding: distributed artificial intelligence embedded into Intelligent or Smart Buildings in an Industry 4.0 application that enables the adaptation to the external environment and the different building users. Buildings are becoming more intelligent in the way they monitor the usage of its assets, functionality and space. The more efficiently a building can be monitored or predicted, the more return of investment can deliver as unused space or energy can be redeveloped or commercialized, therefore reducing energy consumption while increasing functionality. This article proposes distributed artificial intelligence embedded into a Building based on neural networks with a deep learning structure. (1) Sensorial neurons at the device level are dispersed through the intelligent building to gather, filter environment information and predict its next values. (2) Management neurons based on reinforcement learning algorithm at the edge level make predictions about values and trends for building managers or developers to make commercial or operational informed decisions. (3) Finally, transmission neurons based on the genetic algorithms and the genome codify, transmit iBuilding information and also multiplex its data entirely to generate clusters of buildings interconnected with each other at the cloud level. The proposed iBuilding based on distributed learning is validated with a public research dataset; the results show that artificial intelligence embedded into the intelligent building enables real-time monitoring and successful predictions about its variables. The key concept proposed by this article is that the learned information obtained by iBuilding after its adaptation to the environment is never lost when the building changes over time or is decommissioned but transmitted to future generations.

Automated summary

This article introduces iBuilding, a concept of distributed artificial intelligence embedded into intelligent buildings for Industry 4.0 applications, enabling adaptation to external environment and different building users. Through neural networks and deep learning structure, it monitors and predicts building variables for energy efficiency and increased functionality.