Review of artificial intelligence techniques in green/smart buildings
Tài liệu tham khảo
2022
Yudelson, 2009, What is a green building?, Sustainable Retail Development, 41
European Commission, 2015
2016
Gregory, 2003
Hamilton, 2015
Zakari, 2014, Are smart buildings same as green certified buildings? A comparative analysis, Int. J. Sci. Res. Publ., 4, 1
Y. Agarwal, B. Balaji, R. Gupta, J. Lyles, M. Wei, T. Weng, Occupancy driven energy management for smart building automation, in: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, Zurich, Switzerland, Vol. 3, No. 5, 2010, pp. 1–6.
A. Marchiori, Q. Han, Distributed wireless control for building energy management, in: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, Vol. 3, No. 5, Zurich, Switzerland, 2010, pp. 37–42.
Nguyen, 2013, Energy intelligent buildings based on user activity: A survey, Energy Build., 56, 244, 10.1016/j.enbuild.2012.09.005
Harris, 2005, Exploiting user behaviour for context-aware power management, 122
L. Hawarah, S. Ploix, M. Jacomino, User behavior prediction in energy consumption in housing using Bayesian networks, in: 10th International Conference on Artificial Intelligence and Soft Computing, Vol. 13, No. 17, Zakopane, Poland, 2010, pp. 372–379, 2010.
Hagras, 2004, Creating an ambient intelligence environment using embedded agents, IEEE Intell. Syst., 19, 12, 10.1109/MIS.2004.61
Dodier, 2006, Building occupancy detection through sensor belief networks, Energy Build., 38, 1033, 10.1016/j.enbuild.2005.12.001
G. Perrouin, B. Morin, F. Chauvel, F. Fleurey, J. Klein, Y.Le. Traon, O. Barais, J.M. Jezequel, Towards Flexible Evolution of Dynamically Adaptive Systems, in: Proceedings of the 34th IEEE ICSE’12, 2012, pp. 1353–1356.
Cetina, 2009, Autonomic computing through reuse of variability models at runtime: The case of smart homes, Computer, 42, 37, 10.1109/MC.2009.309
Biswas, 2017, Fuzzy decision approach for selection of most suitable construction method of Green Buildings, Int. J. Sustain. Built Environ., 6, 122, 10.1016/j.ijsbe.2017.02.005
Rodríguez-Gracia, 2019, Microservices and machine learning algorithms for adaptive green buildings, Sustainability, 11, 4320, 10.3390/su11164320
N, 2021, How to conduct a bibliometric analysis: An overview and guidelines, J. Bus. Res., 133, 285, 10.1016/j.jbusres.2021.04.070
Kumar, 2022, Artificial intelligence and blockchain integration in business: trends from a bibliometric-content analysis, Inf. Syst. Front., 0, 1
MacCoun, 1998, Biases in the interpretation and use of research results, Ann. Rev. Psychol., 49, 259, 10.1146/annurev.psych.49.1.259
Kraus, 2021, The importance of literature reviews in small business and entrepreneurship research, J. Small Bus. Manag., 0
Visessonchok, 2014, Detection and introduction of emerging technologies for green buildings in Thailand, 620
Liu, 2020, Visualized analysis of knowledge development in green building based on bibliographic data mining, J. Supercomput., 76, 3266, 10.1007/s11227-018-2543-y
Zhou, 2019, A bibliographic analysis of wáter efficiency among green building rating tools: LEED and ESGB, Appl. Ecol. Environ. Res., 17, 11639, 10.15666/aeer/1705_1163911653
Zhao, 2019, A bibliometric review of green building research 2000–2016, Archit. Sci. Rev., 62, 74, 10.1080/00038628.2018.1485548
Darko, 2019, A scientometric analysis and visualization of global green building research, Build. Environ., 149, 501, 10.1016/j.buildenv.2018.12.059
Xiao, 2019, Mapping knowledge in the economic areas of green building using scientometric analysis, Energies, 12, 3011, 10.3390/en12153011
Mukherjee, 2022, Guidelines for advancing theory and practice through bibliometric research, J. Bus. Res., 148, 101, 10.1016/j.jbusres.2022.04.042
Terán-Yépez, 2020, Sustainable entrepreneurship: Review of its evolution and new trends, J. Clean. Prod., 252, 10.1016/j.jclepro.2019.119742
Mongeon, 2016, The journal coverage of web of science and scopus: a comparative analysis, Scientometrics, 106, 213, 10.1007/s11192-015-1765-5
Varkonyi-Koczy, 2019, State of the art of machine learning models in energy systems, a systematic review, Energies, 12, 1301, 10.3390/en12071301
Gusenbauer, 2020, Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources, Res. Synth. Methods, 11, 181, 10.1002/jrsm.1378
Echchakoui, 2020, Why and how to merge Scopus and Web of Science during bibliometric analysis: the case of sales force literature from 1912 to 2019, J. Mark. Anal., 8, 165, 10.1057/s41270-020-00081-9
Kasaraneni, 2022, Automatic merging of scopus and web of science data for simplified and effective bibliometric analysis, Ann. Data Sci., 1
Adams, 2017, Shades of grey: guidelines for working with the grey literature in systematic reviews for management and organizational studies, Int. J. Manag. Rev., 19, 432, 10.1111/ijmr.12102
Waltman, 2012, A new methodology for constructing a publication-level classification system of science, J. Am. Soc. Inf. Sci. Technol., 63, 2378, 10.1002/asi.22748
Waltman, 2010, A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS, J. Am. Soc. Inf. Sci. Technol., 61, 2405, 10.1002/asi.21421
Cobo, 2011, Science mapping software tools: Review, analysis, and cooperative study among tools, J. Am. Soc. Inf. Sci. Technol., 62, 1382, 10.1002/asi.21525
Agramunt, 2020, Review on the relationship of absorptive capacity with interorganizational networks and the internationalization process, Complexity, 2326, 1, 10.1155/2020/7604579
Liao, 2018, A bibliometric analysis and visualization of medical big data research, Sustainability, 10, 166, 10.3390/su10010166
Brown, 2008, Bounded socio-technical experiments as agents of systemic change: The case of a zero-energy residential building, Technol. Forecast. Soc. Change, 75, 107, 10.1016/j.techfore.2006.05.014
Kim, 2018, Personal comfort models – A new paradigm in thermal comfort for occupant-centric environmental control, Build. Environ., 132, 114, 10.1016/j.buildenv.2018.01.023
Mohammadi, 2018, Semisupervised deep reinforcement learning in support of IoT and smart city services, IEEE Internet Things J., 5, 624, 10.1109/JIOT.2017.2712560
Liu, 2019, Intelligent edge computing for IoT-based energy management in smart cities, IEEE Netw., 33, 111, 10.1109/MNET.2019.1800254
Peng, 2018, Using machine learning techniques for occupancy-prediction-based cooling control in office buildings, Appl. Energy, 211, 1343, 10.1016/j.apenergy.2017.12.002
Van Bueren, 2002, Institutional barriers to sustainable construction, Environ. Plann. B Plann. Des., 29, 75, 10.1068/b2785
Dong, 2016, A hybrid model approach for forecasting future residential electricity consumption, Energy Build., 117, 341, 10.1016/j.enbuild.2015.09.033
Fan, 2019, Assessment of deep recurrent neural network-based strategies for short-term building energy predictions, Appl. Energy, 236, 700, 10.1016/j.apenergy.2018.12.004
Dahooie, 2018, A novel approach for evaluation of projects using an interval–valued fuzzy additive ratio assessment (aras) method: a case study of oil and gas well drilling projects, Symmetry, 10, 45, 10.3390/sym10020045
Chen, 2018, A novel ensemble ELM for human activity recognition using smartphone sensors, IEEE Trans. Ind. Inform., 15, 2691, 10.1109/TII.2018.2869843
Minoli, 2017, IoT considerations, requirements, and architectures for smart buildings energy optimization and next-generation building management systems, IEEE Internet Things J., 4, 269, 10.1109/JIOT.2017.2647881
Wortmann, 2015, Internet of things, 221
Cristino, 2018, Energy efficiency in buildings: analysis of scientific literature and identification of data analysis techniques from a bibliometric study, Scientometrics, 114, 1275, 10.1007/s11192-017-2615-4
MacDonald, 2000, Useand valuation: information in the city, Urban Stud., 37, 1881, 10.1080/00420980020080481
Samaniego, 2019, An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications, Comput. Methods Appl. Mech. Eng., 362
2022
2022, European commission