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Title	iBuilding: Artificial Intelligence in Intelligent Buildings
ID_Doc	44393
Authors	Serrano, W
Title	iBuilding: Artificial Intelligence in Intelligent Buildings
Year	2020
Published
Abstract	This paper presents iBuilding: Artificial Intelligence embedded into Intelligent Buildings that adapts 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 efficient a building can be monitored or predicted, the more return of investment can deliver as unused space or energy can be redeveloped or commercialized, reducing energy consumption while increasing functionality. This paper proposes Artificial Intelligence embedded into a Building based on a simple Deep Learning structure and Reinforcement Learning algorithm. Sensorial neurons are dispersed through the Intelligent Building to gather and filter environment information whereas Management Sensors based on Reinforcement Learning algorithm make predictions about values and trends in order for building managers or developers to make commercial or operational informed decisions. The proposed iBuilding is validated with a research dataset. The results show that Artificial Intelligence embedded into the Intelligent Building enables real time monitoring and successful predictions about its variables; although there is further research to improve the algorithm's performance as the results are not optimum.
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ID	Score	Article
44417		Serrano, W iBuilding: artificial intelligence in intelligent buildings(2022)Neural Computing & Applications, 34, 2
30565		Tushar, W; Wijerathne, N; Li, WT; Yuen, C; Poor, HV; Saha, TK; Wood, KL Internet of Things for Green Building Management Disruptive innovations through low-cost sensor technology and artificial intelligence(2018)Ieee Signal Processing Magazine, 35, 5
29211		Baduge, SK; Thilakarathna, S; Perera, JS; Arashpour, M; Sharafi, P; Teodosio, B; Shringi, A; Mendis, P Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications(2022)
16123		Sheikhnejad, Y; Gonçalves, D; Oliveira, M; Martins, N Can buildings be more intelligent than users?-The role of intelligent supervision concept integrated into building predictive control(2020)