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

Intelligent buildings are quickly becoming cohesive and integral inhabitants of cyberphysical 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 bioinspired systems. This article provides an overview of intelligent buildings of the future from a range of perspectives. It discusses everything from the prospects of U.S. and world energy consumption to insights into the future of intelligent buildings based on the latest technological advancements in U.S. industry and government.

[1]  Le Zhang,et al.  Ensemble deep learning for regression and time series forecasting , 2014, 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL).

[2]  Milos Manic,et al.  Building Energy Management Systems: The Age of Intelligent and Adaptive Buildings , 2016, IEEE Industrial Electronics Magazine.

[3]  Milind Tambe,et al.  Human-Building Interaction for Energy Conservation in Office Buildings , 2012 .

[4]  Mahmoud R. Halfawy,et al.  Building Integrated Architecture/Engineering/Construction Systems Using Smart Objects: Methodology and Implementation , 2005 .

[5]  Andreas Wagner,et al.  Thermal comfort and workplace occupant satisfaction—Results of field studies in German low energy office buildings , 2007 .

[6]  Geoffrey E. Hinton Deep belief networks , 2009, Scholarpedia.

[7]  Stephen Cass Big fridge is watching you [smart technologies monitoring food from production to consuption] , 2013, IEEE Spectrum.

[8]  A. Asuncion,et al.  UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences , 2007 .

[9]  C. Clastres Smart grids: Another step towards competition, energy security and climate change objectives , 2011 .

[10]  Ljupco Kocarev,et al.  Deep belief network based electricity load forecasting: An analysis of Macedonian case , 2016 .

[11]  Earlence Fernandes,et al.  Security Analysis of Emerging Smart Home Applications , 2016, 2016 IEEE Symposium on Security and Privacy (SP).

[12]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[13]  P J Webros BACKPROPAGATION THROUGH TIME: WHAT IT DOES AND HOW TO DO IT , 1990 .

[14]  W. Kastner,et al.  The Evolution of Factory and Building Automation , 2011, IEEE Industrial Electronics Magazine.

[15]  Tin Kam Ho,et al.  A Sparse Coding Approach to Household Electricity Demand Forecasting in Smart Grids , 2017, IEEE Transactions on Smart Grid.

[16]  Milos Manic,et al.  Mining Building Energy Management System Data Using Fuzzy Anomaly Detection and Linguistic Descriptions , 2014, IEEE Transactions on Industrial Informatics.

[17]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[18]  D. Bruckner,et al.  Latest trends in integrating building automation and smart grids , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[19]  Madeleine Gibescu,et al.  Deep learning for estimating building energy consumption , 2016 .

[20]  Giuseppe Chidichimo,et al.  Self-adjusting smart windows based on polymer-dispersed liquid crystals , 2009 .

[21]  Friederich Kupzog,et al.  IT-Enabled Integration of Renewables: A Concept for the Smart Power Grid , 2011, EURASIP J. Embed. Syst..

[22]  Geoffrey E. Hinton,et al.  Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.

[23]  Grant Hernandez,et al.  Smart Nest Thermostat A Smart Spy in Your Home , 2014 .

[24]  Pierluigi Siano,et al.  Demand response and smart grids—A survey , 2014 .

[25]  Milos Manic,et al.  Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique , 2011 .

[26]  Dietmar Dietrich,et al.  Cognitive Automation—Survey of Novel Artificial General Intelligence Methods for the Automation of Human Technical Environments , 2012, IEEE Transactions on Industrial Informatics.

[27]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[28]  David Fisk Cyber security, building automation, and the intelligent building , 2012 .

[29]  Milos Manic,et al.  Artificial neural networks based thermal energy storage control for buildings , 2015, IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society.

[30]  M. Manic,et al.  WESBES: A wireless embedded sensor for improving human comfort metrics using temporospatially correlated data , 2012, 2012 5th International Symposium on Resilient Control Systems.

[31]  Milind Tambe,et al.  Towards Optimal Planning for Distributed Coordination Under Uncertainty in Energy Domains , 2011 .

[32]  Daniel L. Marino,et al.  Building energy load forecasting using Deep Neural Networks , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[33]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[34]  Thilo Sauter,et al.  SmartFridge: Demand Side Management for the device level , 2011, ETFA2011.

[35]  Wei-Peng Chen,et al.  Neural network model ensembles for building-level electricity load forecasts , 2014 .