Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting

Journal Article · Wed Dec 21 00:00:00 EST 2016 · IEEE Industrial Electronics Magazine

DOI:https://doi.org/10.1109/MIE.2016.2615575· OSTI ID:1402466

Manic, Milos ^[1]; Amarasinghe, Kasun ^[1]; Rodriguez-Andina, Juan ^[2]; Rieger, Craig G. ^[3]

---

1. Virginia Commonwealth Univ., Richmond, VA (United States)
2. Univ. of Vigo, Pontevedra (Spain)
3. Idaho National Lab. (INL), Idaho Falls, ID (United States)

This study discuss, intelligent buildings are quickly becoming cohesive and integral entities of cyber-physical ecosystems. Modern buildings adapt to internal and external elements and thrive on ever increasing data sources such as ubiquitous smart devices and sensors while mimicking various approaches previously known in software, hardware, and bio inspired systems. This article provides an overview of intelligent buildings of the future from a range of perspectives. First, a brief overview of prospects of U.S. and world energy consumption, concepts of smart buildings as integral entities of smart grids is presented. This is followed by a discussion of the role of connectivity and interoperability as well as current issues and solutions in the areas of security, resilience, and humans in realm of intelligent buildings. The article dives into detail about the intelligence aspect of intelligent buildings of the future. The learning and predictive Artificial Intelligence (AI) techniques behind many of the concepts of intelligent buildings are reviewed, including the latest techniques such as deep learning. As a concrete application of AI in the realm of intelligent buildings, prediction of energy consumption/load/demand, which is a much researched application of AI in the realm of intelligent buildings, is elaborated. The discussion is focused on most recent advancements of AI based demand prediction and the overview consists of existing research and a case study conducted by the authors for deep learning based demand prediction. Then, the article discusses the role of humans in intelligent buildings. Different types of human-building interactions are identified. Further, the article presents how each type of human-building interaction can benefit the overall control of intelligent buildings. The section is concluded by elaborating a case study conducted by the authors to achieve effective and efficient human-building interaction. The article is concluded with compilation of future insights based on latest technological advancements in U.S. industry and government.

• Research Organization: Idaho National Lab. (INL), Idaho Falls, ID (United States)
• Sponsoring Organization: USDOE Office of Nuclear Energy (NE)
• Grant/Contract Number: AC07-05ID14517
• OSTI ID: 1402466
• Report Number(s): INL/JOU-16-40160
• Journal Information: IEEE Industrial Electronics Magazine, Vol. 10, Issue 4; ISSN 1932-4529
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
97 MATHEMATICS AND COMPUTING
machine learning
computational intelligence
building