survey

Machine Learning for Smart Building Applications: Review and Taxonomy

Authors:

Djamel Djenouri,

Roufaida Laidi,

Youcef Djenouri,

Ilangko BalasinghamAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 52, Issue 2

Article No.: 24, Pages 1 - 36

https://doi.org/10.1145/3311950

Published: 27 March 2019 Publication History

Abstract

The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field.

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References

[1]

2018. Powersmiths: Power for the Future. Retrieved July 12, 2018 from https://ww2.powersmiths.com/index.php?q=content/powesmiths/about-us.

[2]

2018. Weather Data 8 Weather Data Processing Utility Programs. Retrieved July 12, 2018 from http://doe2.com/index_wth.html.

[3]

Rakesh Agrawal, Tomasz Imieliński, and Arun Swami. 1993. Mining association rules between sets of items in large databases. In ACM SIGMOD Record, Vol. 22. ACM, 207--216.

Digital Library

[4]

Alaa Alhamoud, Pei Xu, Frank Englert, Philipp Scholl, The An Binh Nguyen, Doreen Böhnstedt, and Ralf Steinmetz. 2015. Evaluation of user feedback in smart home for situational context identification. In 2015 International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops. IEEE, St. Louis, MO, 20--25.

[5]

Ethem Alpaydin. 2014. Introduction to Machine Learning. The MIT Press, Cambridge, MA.

Digital Library

[6]

K. Anderson, A. Ocneanu, Diego Benitez, D. Carlson, A. Rowe, and M. Berges. 2012. BLUED: A fully labeled public dataset for event-based non-intrusive load monitoring research. In Proceedings of the 2nd KDD Workshop on Data Mining Applications in Sustainability (SustKDD'12). 1--5.

[7]

M. Antunes, D. G. Gomes, and R. Aguiar. 2013. Towards behaviour inference in smart environments. In The Conference on Future Internet Communications-CFIC. IEEE, Coimbra, Portugal, 1--8.

[8]

Daniel B. Araya, KatarinaGrolinger, Hany F. El Yamany, Miriam A. M. Capretz, and Girma Bitsuamlak. 2017. An ensemble learning framework for anomaly detection in building energy consumption. Energy and Buildings 144 (June 2017), 191--206.

[9]

Omid Ardakanian, Arka Bhattacharya, and David Culler. 2016. Non-intrusive techniques for establishing occupancy related energy savings in commercial buildings. In Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys’16). ACM, NY, 21--30.

Digital Library

[10]

Miloud Bagaa, Ali Chelli, Djamel Djenouri, Tarik Taleb, Ilangko Balasingham, and Kimmo Kansanen. 2017. Optimal placement of relay nodes over limited positions in wireless sensor networks. IEEE Trans. Wireless Communications 16, 4 (2017), 2205--2219.

Digital Library

[11]

Dustin Bales, Pablo A. Tarazaga, Mary Kasarda, Dhruv Batra, A. G. Woolard, Jeffrey D. Poston, and V. V. N. S. Malladi. 2016. Gender classification of walkers via underfloor accelerometer measurements. IEEE Internet of Things Journal 3, 6 (2016), 1259--1266.

[12]

Wolfgang Banzhaf, Frank D. Francone, Robert E. Keller, and Peter Nordin. 1998. Genetic Programming: An Introduction: On the Automatic Evolution of Computer Programs and Its Applications. Morgan Kaufmann Publishers Inc., San Francisco, CA.

Digital Library

[13]

Antimo Barbato, L. Borsani, and Antonio Capone. 2010. A wireless sensor network based system for reducing home energy consumption. In Proceedings of the 7th Annual Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON, June. IEEE, Boston, MA, 1--3.

[14]

Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and Jörg Sander. 2000. LOF: Identifying density-based local outliers. In ACM SIGMOD Record, Vol. 29. ACM, 93--104.

Digital Library

[15]

David Caicedo and Ashish Pandharipande. 2015. Sensor-driven lighting control with illumination and dimming constraints. IEEE Sensors Journals 15, 9 (2015), 5169--5176.

[16]

J. Canny. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 6 (1986), 679--698.

Digital Library

[17]

Miriam A. M. Capretz and Girma T. Bitsuamlak. 2016. Collective contextual anomaly detection framework for smart buildings. In International Joint Conference on Neural Networks (IJCNN’16), July 24-29. IEEE, Vancouver, BC, Canada, 511--518.

[18]

Berardina De Carolis, Stefano Ferilli, and Domenico Redavid. 2015. Incremental learning of daily routines as workflows in a smart home environment. ACM Trans. Interact. Intell. Syst. 4, 4 (Jan 2015), 20:1--20:23.

Digital Library

[19]

Vikas Chandan, Arun Vishwanath, Min Zhang, and Shivkumar Kalyanaraman. 2015. Short paper: Data driven pre-cooling for peak demand reduction in commercial buildings. In Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments (BuildSys’15). ACM, New York, NY, 187--190.

Digital Library

[20]

Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 3 (May 2011), 27.

Digital Library

[21]

Yi-Ting Chiang, Ching-Hu Lu, and Jane Yung-jen Hsu. 2017. A feature-based knowledge transfer framework for cross-environment activity recognition toward smart home applications. IEEE Trans. Human-Machine Systems 47, 3 (2017), 310--322.

[22]

Jui-Sheng Chou and Ngoc-Tri Ngo. 2016. Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns. Applied Energy 177 (Sep 2016), 751--770.

[23]

Diane J. Cook. 2012. Learning setting-generalized activity models for smart spaces. IEEE Intelligent Systems 27, 1 (2012), 32--38.

Digital Library

[24]

Stephen Dawson-Haggerty, Jorge Ortiz, Jason Trager, David E. Culler, and Randy H. Katz. 2012. Energy savings and the “software-defined” building. IEEE Design 8 Test of Computers 29, 4 (2012), 56--57.

Digital Library

[25]

Richard De Dear and Gail Schiller Brager. 1998. Developing an adaptive model of thermal comfort and preference. UC Berkeley, Center for the Built Environment. https://escholarship.org/uc/item/4qq2p9c6.

[26]

Alessandra De Paola, Marco Ortolani, Giuseppe Lo Re, Giuseppe Anastasi, and Sajal K. Das. 2014. Intelligent management systems for energy efficiency in buildings: A survey. Comput. Surveys 47, 1 (Jun 2014), 13:1--13:38.

Digital Library

[27]

Dua Dheeru and Efi Karra Taniskidou. 2017. UCI Machine Learning Repository. Retrieved February 12, 2019 from http://archive.ics.uci.edu/ml.

[28]

Djamel Djenouri and Miloud Bagaa. 2016. Synchronization protocols and implementation issues in wireless sensor networks: A review. IEEE Systems Journal 10, 2 (2016), 617--627.

[29]

J. Doak. 1992. An Evaluation of Feature Selection Methods and Their Application to Computer Security. University of California, Computer Science. https://books.google.dz/books?id=S_zhtgAACAAJ.

[30]

Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD’96). AAAI Press, Portland, OR, 226--231.

Digital Library

[31]

Cheng Fan, Fu Xiao, and Yang Zhaoc. 2017. A short-term building cooling load prediction method using deep learning algorithms. Applied Energy 195 (Jun 2017), 222--233.

[32]

Maria Pia Fanti, Gregory Faraut, Jean-Jacques Lesage, and Michele Roccotelli. 2018. An integrated framework for binary sensor placement and inhabitants location tracking. IEEE Trans. on Systems, Man, and Cybernetics: Systems 48, 1 (2018), 154--160.

[33]

Afreen Ferdoash, Shubham Saini, Jitesh Khurana, and Amarjeet Singh. 2015. Poster abstract: Analytics driven operational efficiency in HVAC systems. In Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments (BuildSys’15). ACM, NY, 107--108.

Digital Library

[34]

Stefano Ferilli. 2014. WoMan: Logic-based workflow learning and management. IEEE Trans. Systems, Man, and Cybernetics: Systems 44, 6 (2014), 744--756.

[35]

D. Gale and L. S. Shapley. 1962. College admissions and the stability of marriage. Amer. Math. Monthly 69, 1 (1962), 9--15.

[36]

Jingkun Gao, Joern Ploennigs, and Mario Berges. 2015. A data-driven meta-data inference framework for building automation systems. In Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments (BuildSys’15). ACM, Seoul, South Korea, 23--32.

Digital Library

[37]

Ali Ghahramani, Chao Tanga, and Burcin Becerik-Gerberb. 2015. An online learning approach for quantifying personalized thermal comfort via adaptive stochastic modeling. Building and Environment 92 (Oct 2015), 86--96.

[38]

Luis I. Lopera Gonzalez, Reimar Stier, and Oliver Amft. 2016. Data mining-based localisation of spatial low-resolution sensors in commercial buildings. In Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys’16). ACM, New York, NY, 187--196.

Digital Library

[39]

Jihun Hamm, Adam C. Champion, Guoxing Chen, Mikhail Belkin, and Dong Xuan. 2015. Crowd-ML: A privacy-preserving learning framework for a crowd of smart devices. In 35th International Conference on Distributed Computing Systems, ICDCS, June 29 - July 2. IEEE, Columbus, OH, 11--20.

[40]

Tom Hargreavesn, Michael Nye, and Jacquelin Burgess. 2013. Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term. Energy Policy 52 (Jan 2013), 126--134.

[41]

Olivier Hersent, David Boswarthick, and Omar Elloumi. 2012. The Internet of Things: Key Applications and Protocols (2nd ed.). Wiley Publishing, NJ.

Digital Library

[42]

Dezhi Hong, Hongning Wang, Jorge Ortiz, and Kamin Whitehouse. 2015. The building adapter: Towards quickly applying building analytics at scale. In Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments (BuildSys’15), Seoul, South Korea, November 4-5, 2015. ACM, NY, 123--132.

Digital Library

[43]

H. M. Sajjad Hossain, M. D. Abdullah Al Hafiz Khan, and Nirmalya Roy. 2017. Active learning enabled activity recognition. Pervasive and Mobile Computing 38 (2017), 312--330.

[44]

Aapo Hyvarinen. 1999. Survey on independent component analysis. Neural Computing Surveys 2, 4 (1999), 94--128.

[45]

Srinivasan Iyengar, Stephen Lee, David Irwin, and Prashant Shenoy. 2016. Analyzing energy usage on a city-scale using utility smart meters. In Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys’16). ACM, NY, 51--60.

Digital Library

[46]

Achin Jain, Rahul Mangharam, and Madhur Behl. 2016. Data predictive control for peak power reduction. In Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys’16). ACM, NY, 109--118.

Digital Library

[47]

A. K. Jain, M. N. Murty, and P. J. Flynn. 1999. Data clustering: A review. ACM Comput. Surv. 31, 3 (Sep 1999), 264--323.

Digital Library

[48]

F. Jazizadeh, F. M. Marin, and B. Becerik-Gerber. 2013. A thermal preference scale for personalized comfort profile identification via participatory sensing. Building and Environment 9 (Oct 2013), 68--140.

[49]

Farrokh Jazizadeh and S. Pradeep. 2016. Can computers visually quantify human thermal comfort?: Short paper. In Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys@SenSys. ACM, NY, 95--98.

Digital Library

[50]

Simin Ahmadi Karvigha, Ali Ghahramania, Burcin Becerik Gerberb, and Lucio Soibelman. 2017. One size does not fit all: Understanding user preferences for building automation systems. Energy and Buildings 145, 15 (Jun 2017), 163--173.

[51]

Maria Kazandjieva, Brandon Heller, Philip Levis, and Christos Kozyrakis. 2009. Energy dumpster diving. In 2nd Workshop on Power Aware Computing (HotPower). ACM, 1--5.

[52]

Aqeel H. Kazmi, Michael J. O’Grady, Declan T. Delaney, Antonio G. Ruzzelli, and Gregory M. P. O’Hare. 2014. A review of wireless-sensor-network-enabled building energy management systems. ACM Transactions on Sensor Networks 10, 4 (Jun 2014), 1--43.

Digital Library

[53]

Nacer Khalil, Driss Benhaddou, Omprakash Gnawali, and Jaspal Subhlok. 2016. Nonintrusive occupant identification by sensing body shape and movement. In Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys’16). ACM, New York, NY, 1--10.

Digital Library

[54]

Aftab Khan, James Nicholson, Sebastian Mellor, Daniel Jackson, Karim Ladha, Cassim Ladha, Jon Hand, Joseph Clarke, Patrick Olivier, and Thomas Plötz. 2014. Occupancy monitoring using environmental and context sensors and a hierarchical analysis framework. In Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings (BuildSys’14). ACM, New York, NY, 90--99.

Digital Library

[55]

Rida Khatoun and Sherali Zeadally. 2016. Smart cities: Concepts, architectures, research opportunities. Commun. ACM 59, 8 (Aug 2016), 46--57.

Digital Library

[56]

Timilehin Labeodan, Wim Zeiler, Gert Boxem, and Yang Zhao. 2015. Occupancy measurement in commercial office buildings for demand-driven control applications —a survey and detection system evaluation. Energy and Buildings 93 (April 2015), 303--314.

[57]

Henning Lange and Mario Bergés. 2016. BOLT: Energy disaggregation by online binary matrix factorization of current waveforms. In Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys’16). ACM, New York, NY, 11--20.

Digital Library

[58]

A. Laurucci, S. Melzi, and M. Cesana. 2009. A reconfigurable middleware for dynamic management of heterogeneous applications in multi-gateway mobile sensor networks. In Proceedings of the 7th Annual Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON’09). IEEE, Rome, Italy, 1--3.

[59]

Sanja Lazarova-Molnar, Mikkel Baun Kjærgaard, Hamid Reza Shaker, and Bo Nørregaard Jørgensen. 2015. Commercial buildings energy performance within context occupants in spotlight. In 2015 International Conference on Smart Cities and Green ICT Systems (SMARTGREENS’15). IEEE, 1--7.

Digital Library

[60]

Sanja Lazarova Molnar, Halldor Logason, Peter Grønbæk Andersen, and Mikkel Baun Kjærgaard. 2017. Mobile crowdsourcing of occupant feedback in smart buildings. SIGAPP Appl. Comput. Rev. 17, 1 (May 2017), 5--14.

Digital Library

[61]

Dan Li, Yuxun Zhou, Guoqiang Hu, and Costas J. Spanos. 2017. Optimal sensor configuration and feature selection for AHU fault detection and diagnosis. IEEE Trans. Industrial Informatics 13, 3 (2017), 1369--1380.

[62]

Woong-Kee Loh and Young-Ho Park. 2014. A survey on density-based clustering algorithms. In Ubiquitous Information Technologies and Applications. Springer, 775--780.

[63]

Clayton Miller, Zoltán Nagy, and Arno Schlueter. 2018. A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings. Renewable and Sustainable Energy Reviews 81 (2018), 1365--1377.

[64]

Thomas M. Mitchell. 1997. Machine Learning (1st ed.). McGraw-Hill, Inc., New York, NY.

Digital Library

[65]

Elena Mocanu, Phuong H. Nguyenv, Madeleine Gibescu, and Wil L. Kling. 2016. Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks 6 (Jun 2016), 91--99.

[66]

Sanja Lazarova Molnar, Mikkel Baun Kjaergaard, Hamid Reza Shaker, and Bo Norregaard Jorgensen. 2015. Commercial buildings energy performance within context—occupants in spotlight. In Proceedings of the 4th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS’15), 20-22 May. IEEE, Lisbon, Portugal, 306--312.

Digital Library

[67]

Volodymyr Mnih et al. 2015. Human-level control through deep reinforcement learning. Nature 518, 7540 (2015).

[68]

Ahmed Nait Aicha, Gwenn Englebienne, and Ben Kröse. 2017. Unsupervised visit detection in smart homes. Pervasive Mob. Comput. 34 (2017), 157--167.

Digital Library

[69]

Kaoru Ota, Minh Son Dao, Vasileios Mezaris, and Francesco G. B. De Natale. 2017. Deep learning for mobile multimedia: A survey. ACM Trans. Multimedia Comput. Commun. Appl. 13, 3s (Jun 2017), 34:1--34:22.

Digital Library

[70]

Abdelraouf Ouadjaout, Noureddine Lasla, Djamel Djenouri, and Cherif Zizoua. 2016. On the effect of sensing-holes in PIR-based occupancy detection systems. In SENSORNETS 2016-Proceedings of the 5th International Conference on Sensor Networks, February 19-21, 2016. Rome, Italy, 175--180.

Digital Library

[71]

Charith Perera, Chi Harold Liu, and Srimal Jayawardena. 2015. The emerging Internet of Things marketplace from an industrial perspective: A survey. IEEE Trans. Emerg. Top. Comput. 3, 4 (Oct. 2015), 585--598.

Digital Library

[72]

Charith Perera, Yongrui Qin, Julio C. Estrella, Stephan Reiff-Marganiec, and Athanasios V. Vasilakos. 2017. Fog computing for sustainable smart cities: A survey. ACM Comput. Surv. 50, 3, Article 32 (June 2017), 43 pages.

Digital Library

[73]

Vasso Reppa, Panayiotis M. Papadopoulos, Marios M. Polycarpou, and Christos G. Panayiotou. 2015. A distributed architecture for HVAC sensor fault detection and isolation. IEEE Trans. Contr. Sys. Techn. 23, 4 (2015), 1323--1337.

[74]

Antonio Ridi, Christophe Gisler, and Jean Hennebert. 2015. Processing smart plug signals using machine learning. In 2015 Wireless Communications and Networking Conference Workshops, WCNC Workshops 2015, New Orleans, LA, March 9-12, 2015. IEEE, 75--80.

[75]

Robert H. Shumway and David S. Stoffer. 2017. Time Series Analysis and Its Applications: With R Examples (4th ed.). Springer.

[76]

Fisayo Caleb Sangogboye, Kenan Imamovic, and Mikkel Baun Kjærgaard. 2016. Improving occupancy presence prediction via multi-label classification. In International Conference on Pervasive Computing and Communication (PerCom’16) Workshops. IEEE, Sydney, Australia, 1--6.

[77]

Chayan Sarkar, Akshay Uttama Nambi S. N., and Venkatesha Prasad. 2016. iLTC: Achieving individual comfort in shared spaces. In Proceedings of the 2016 International Conference on Embedded Wireless Systems and Networks (EWSN’16). Junction Publishing, 65--76. http://dl.acm.org/citation.cfm?id=2893711.2893723

Digital Library

[78]

James Scott, A. J. Bernheim Brush, John Krumm, Brian Meyers, Michael Hazas, Stephen Hodges, and Nicolas Villar. 2011. PreHeat: Controlling home heating using occupancy prediction. In Proceedings of the 13th International Conference on Ubiquitous Computing (UbiComp’11). ACM, New York, NY, 281--290.

Digital Library

[79]

Olli Seppanen, William J. Fisk, and David Faulkner. 2004. Control of temperature for health and productivity in offices. ASHRAE Transactions.

[80]

Oliver Shih, Patrick Lazik, and Anthony Rowe. 2016. AURES: A wide-band ultrasonic occupancy sensing platform. In Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys’16). ACM, New York, NY, 157--166.

Digital Library

[81]

Tomoaki Shoji, Wataru Hirohashi, Yu Fujimoto, and Yasuhiro Hayashi. 2014. Home energy management based on Bayesian network considering resident convenience. In International Conference on Probabilistic Methods Applied to Power Systems (PMAPS’14). IEEE, Durham, UK, 1--6.

[82]

Elahe Soltanaghaei and Kamin Whitehouse. 2016. WalkSense: Classifying home occupancy states using walkway sensing. In Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys’16). ACM, New York, NY, 167--176.

Digital Library

[83]

Yusuf Sonmez, Ugur Guvenc, H. Tolga Kahraman, and Cemal Yilmaz. 2015. A comparative study on novel machine learning algorithms for estimation of energy performance of residential buildings. In 3rd International IEEE Conference on Smart Grid Congress and Fair (ICSG’15). IEEE, Istanbul, 1--7.

[84]

Fuchen Sun, Kar-Ann Toh, Manuel Grana Romay, and Kezhi Mao (eds.). 2014. Extreme Learning Machines 2013: Algorithms and Applications. Springer.

Digital Library

[85]

Zhongfu Tan, Jinliang Zhang, Jianhui Wang, and Jun Xu. 2010. Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models. Applied Energy 87, 11 (2010), 3606--3610.

[86]

Emmanuel Munguia Tapia, Stephen S. Intille, and Kent Larson. 2004. Activity recognition in the home using simple and ubiquitous sensors. In Pervasive Computing, Alois Ferscha and Friedemann Mattern (Eds.). Springer, Berlin, 158--175.

[87]

Trevor Hastie, Robert Tibshirani, and Jerome Friedman. 2013. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.

[88]

Tim van Kasteren, Athanasios Noulas, Gwenn Englebienne, and Ben Kröse. 2008. Accurate activity recognition in a home setting. In Proceedings of the 10th International Conference on Ubiquitous Computing (UbiComp’08). ACM, New York, NY, 1--9.

Digital Library

[89]

Yongcai Wang, Xiaohong Hao, Lei Song, Chenye Wu, Yuexuan Wang, Changjian Hu, and Lu Yu. 2014. Monitoring massive appliances by a minimal number of smart meters. ACM Trans. Embed. Comput. Syst. 13, 2s (Jan 2014), 56:1--56:20.

Digital Library

[90]

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma. 2009. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 2 (Feb 2009), 210--227.

Digital Library

[91]

David P. Wyon. 2004. The effects of indoor air quality on performance and productivity. Indoor Air 14, 7 (2004), 92--101.

[92]

Jiang Xiao, Zimu Zhou, Youwen Yi, and Lionel M. Ni. 2016. A survey on wireless indoor localization from the device perspective. ACM Comput. Surv. 49, 2, Article 25 (June 2016), 31 pages.

Digital Library

[93]

Jeonghee Yi, Tetsuya Nasukawa, Razvan Bunescu, and Wayne Niblack. 2003. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In 3rd IEEE International Conference on Data Mining (ICDM’03). IEEE, 427--434.

Digital Library

[94]

Dong Zhang, Shuhui Li, Min Sun, and Zheng O’Neill. 2016. An optimal and learning-based demand response and home energy management system. IEEE Trans. Smart Grid 7, 4 (2016), 1790--1801.

[95]

Yuxun Zhou, Dan Li, and Costas J. Spanos. 2015. Learning optimization friendly comfort model for HVAC model predictive control. In International Conference on Data Mining Workshop (ICDMW’15). IEEE, Atlantic City, NJ, 430--439.

Digital Library

[96]

Qingchang Zhu, Zhenghua Chen, and Yeng Chai Soh. 2015. Smartphone-based human activity recognition in buildings using locality-constrained linear coding. In 10th International Conference on Industrial Electronics and Applications (ICIEA’15). IEEE, Auckland, New Zealand, 214--219.

[97]

B. Yildiz, J. I. Bilbao, and A. B. Sproul. 2017. A review and analysis of regression and machine learning models on commercial building electricity load forecasting. Renewable and Sustainable Energy Reviews 73 (June 2017), 1104--1122.

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ACM Computing Surveys Volume 52, Issue 2

March 2020

770 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3320149

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Publication History

Published: 27 March 2019

Accepted: 01 December 2018

Revised: 01 August 2018

Received: 01 February 2018

Published in CSUR Volume 52, Issue 2

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Bilésimo TGhisi E(2024)Utilisation of Machine Learning in Control Systems Based on the Preference of Office UsersSustainability10.3390/su1610425816:10(4258)Online publication date: 18-May-2024
https://doi.org/10.3390/su16104258
Ożadowicz A(2024)Generic IoT for Smart Buildings and Field-Level Automation—Challenges, Threats, Approaches, and SolutionsComputers10.3390/computers1302004513:2(45)Online publication date: 3-Feb-2024
https://doi.org/10.3390/computers13020045
Zhu JYuan YWang FWang G(2024)Federated Control: A Trustable Control Framework for Large-Scale Cyber-Physical SystemsIEEE Transactions on Industrial Informatics10.1109/TII.2024.336309220:5(7986-7994)Online publication date: May-2024
https://doi.org/10.1109/TII.2024.3363092
Belhadi ADjenouri DDjenouri YBelbachir ASrivastava G(2024)Social Web in IoT: Can Evolutionary Computation and Clustering Improve Ontology Matching for Social Web of Things?IEEE Transactions on Computational Social Systems10.1109/TCSS.2023.333256211:3(3966-3977)Online publication date: Jul-2024
https://doi.org/10.1109/TCSS.2023.3332562
Djenouri YYazidi ASrivastava GLin J(2024)Blockchain: Applications, Challenges, and Opportunities in Consumer ElectronicsIEEE Consumer Electronics Magazine10.1109/MCE.2023.324791113:2(36-41)Online publication date: Mar-2024
https://doi.org/10.1109/MCE.2023.3247911
Dridi JAmayri MBouguila N(2024)Unsupervised domain adaptation without source data for estimating occupancy and recognizing activities in smart buildingsEnergy and Buildings10.1016/j.enbuild.2023.113808303(113808)Online publication date: Jan-2024
https://doi.org/10.1016/j.enbuild.2023.113808
Vering CGöbel SKlebig TWill FHorst JWüllhorst FNürenberg MMehrfeld PMüller D(2024)Towards a defossilized building sector with field tests in the lab: Review, development, and evaluationApplied Energy10.1016/j.apenergy.2024.123225365(123225)Online publication date: Jul-2024
https://doi.org/10.1016/j.apenergy.2024.123225
Xiao TYou F(2024)Physically consistent deep learning-based day-ahead energy dispatching and thermal comfort control for grid-interactive communitiesApplied Energy10.1016/j.apenergy.2023.122133353(122133)Online publication date: Jan-2024
https://doi.org/10.1016/j.apenergy.2023.122133
Yang XMaiti AKist A(2024)A Reconfigurable Strategy for Internet-of-Things for Smart BuildingsOpen Science in Engineering10.1007/978-3-031-42467-0_74(781-794)Online publication date: 1-Jan-2024
https://doi.org/10.1007/978-3-031-42467-0_74
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