Uneingeschränkter Zugang

An Evaluation of Artificial Intelligence Integrated in Control Strategies in Building Services


Zitieren

[1] Directive 2018/844/EU of the European Parliament and of the Council of 30 May 2018 Amending Directive 2010/31/EU on the Energy Performance of Buildings and Directive 2012/27/EU on Energy Efficiency, Official Journal of the European Union 61, 19.6.2018, pp. 43–74. Accessible online at: http://data.europa.eu/eli/dir/2018/844/oj Search in Google Scholar

[2] Minoli, D., Sohraby, K., & Occhiogrosso, B. (2017). IoT Considerations, Requirements, and Architectures for Smart Buildings—Energy Optimization and Next-Generation Building Management Systems. IEEE Internet Of Things Journal, 4(1), 269-283. https://doi.org/10.1109/jiot.2017.264788110.1109/JIOT.2017.2647881 Search in Google Scholar

[3] Mariano-Hernández, D., Hernández-Callejo, L., Zorita-Lamadrid, A., Duque-Pérez, O., & Santos García, F. (2021). A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis. Journal of Building Engineering, 33, 101692. https://doi.org/10.1016/j.jobe.2020.10169210.1016/j.jobe.2020.101692 Search in Google Scholar

[4] Malekpour Koupaei, D., Song, T., Cetin, K., & Im, J. (2020). An assessment of opinions and perceptions of smart thermostats using aspect-based sentiment analysis of online reviews. Building And Environment, 170, 106603. https://doi.org/10.1016/j.buildenv.2019.10660310.1016/j.buildenv.2019.106603 Search in Google Scholar

[5] Halhoul Merabet, G., Essaaidi, M., Ben Haddou, M., Qolomany, B., Qadir, J., & Anan, M. et al. (2021). Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques. Renewable and Sustainable Energy Reviews, 144, 110969. https://doi.org/10.1016/j.rser.2021.11096910.1016/j.rser.2021.110969 Search in Google Scholar

[6] Ethical machines: The human-centric use of artificial intelligence. (2021). Iscience, (102249). https://doi.org/10.1016/j.isci.2021.10224910.1016/j.isci.2021.102249797385933763636 Search in Google Scholar

[7] Aguilar, J., Garces-Jimenez, A., R-Moreno, M., & García, R. (2021). A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings. Renewable And Sustainable Energy Reviews, 151, 111530. https://doi.org/10.1016/j.rser.2021.11153010.1016/j.rser.2021.111530 Search in Google Scholar

[8] Engineer, A., Gualano, R., Crocker, R., Smith, J., Maizes, V., Weil, A., & Sternberg, E. (2021). An integrative health framework for wellbeing in the built environment. Building and Environment, 205, 108253. https://doi.org/10.1016/j.buildenv.2021.10825310.1016/j.buildenv.2021.108253 Search in Google Scholar

[9] Gyeong Yun, Kap Chun Yoon, Kang Soo Kim (2014). The influence of shading control strategies on the visual comfort and energy demand of office buildings, Energy and Buildings, Volume 84, Pages 70-85, ISSN 0378-7788, https://doi.org/10.1016/j.enbuild.2014.07.04010.1016/j.enbuild.2014.07.040 Search in Google Scholar

[10] Lee, H., Wu, C., & Aghajan, H. (2011). Vision-based user-centric light control for smart environments. Pervasive And Mobile Computing, 7(2), 223-240. https://doi.org/10.1016/j.pmcj.2010.08.00310.1016/j.pmcj.2010.08.003 Search in Google Scholar

[11] Chiesa, G., Di Vita, D., Ghadirzadeh, A., Muñoz Herrera, A., & Leon Rodriguez, J. (2020). A fuzzy-logic IoT lighting and shading control system for smart buildings. Automation In Construction, 120, 103397. https://doi.org/10.1016/j.autcon.2020.10339710.1016/j.autcon.2020.103397 Search in Google Scholar

[12] Motamed, A., Deschamps, L., & Scartezzini, J. (2019). Eight-month experimental study of energy impact of integrated control of sun shading and lighting system based on HDR vision sensor. Energy And Buildings, 203, 109443. https://doi.org/10.1016/j.enbuild.2019.10944310.1016/j.enbuild.2019.109443 Search in Google Scholar

[13] Chaouch, H., Çeken, C., & Arı, S. (2021). Energy management of HVAC systems in smart buildings by using fuzzy logic and M2M communication. Journal of Building Engineering, 44, 102606. https://doi.org/10.1016/j.jobe.2021.10260610.1016/j.jobe.2021.102606 Search in Google Scholar

[14] Li, W., Zhang, J., Zhao, T., & Ren, J. (2021). Experimental study of an indoor temperature fuzzy control method for thermal comfort and energy saving using wristband device. Building and Environment, 187, 107432. https://doi.org/10.1016/j.buildenv.2020.10743210.1016/j.buildenv.2020.107432 Search in Google Scholar

[15] Jassar, S., Liao, Z., & Zhao, L. (2009). Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems. Building and Environment, 44(8), 1609-1616. https://doi.org/10.1016/j.buildenv.2008.10.00210.1016/j.buildenv.2008.10.002 Search in Google Scholar

[16] Macarulla, M., Casals, M., Forcada, N., & Gangolells, M. (2017). Implementation of predictive control in a commercial building energy management system using neural networks. Energy and Buildings, 151, 511-519. https://doi.org/10.1016/j.enbuild.2017.06.02710.1016/j.enbuild.2017.06.027 Search in Google Scholar

[17] Han Zou, Yuxun Zhou, Hao Jiang, Szu-Cheng Chien, Lihua Xie, Costas J. Spanos (2018). WinLight: A WiFi-based occupancy-driven lighting control system for smart building, Energy and Buildings, Volume 158, Pages 924-938, ISSN 0378-7788, https://doi.org/10.1016/j.enbuild.2017.09.00110.1016/j.enbuild.2017.09.001 Search in Google Scholar

[18] Manar Amayri, Stephane Ploix, Nizar Bouguila, Frederic Wurtz (2020). Database quality assessment for interactive learning: Application to occupancy estimation, Energy and Buildings, Volume 209, 109578,ISSN 0378-7788, https://doi.org/10.1016/j.enbuild.2019.10957810.1016/j.enbuild.2019.109578 Search in Google Scholar

[19] Hong, T., Wang, Z., Luo, X., & Zhang, W. (2020). State-of-the-art on research and applications of machine learning in the building life cycle. Energy and Buildings, 212, 109831. https://doi.org/10.1016/j.enbuild.2020.10983110.1016/j.enbuild.2020.109831 Search in Google Scholar

[20] Chujie Lu, Sihui Li, Zhengjun Lu (2022). Building energy prediction using artificial neural networks: A literature survey, Energy and Buildings, Volume 262, 111718, ISSN 0378-7788, https://doi.org/10.1016/j.enbuild.2021.11171810.1016/j.enbuild.2021.111718 Search in Google Scholar

[21] Eseye, A., & Lehtonen, M. (2020). Short-Term Forecasting of Heat Demand of Buildings for Efficient and Optimal Energy Management Based on Integrated Machine Learning Models. IEEE Transactions on Industrial Informatics, 16(12), 7743-7755. https://doi.org/10.1109/tii.2020.297016510.1109/TII.2020.2970165 Search in Google Scholar

[22] Sara M.C. Magalhães, Vítor M.S. Leal, Isabel M. Horta (2017). Modelling the relationship between heating energy use and indoor temperatures in residential buildings through Artificial Neural Networks considering occupant behavior, Energy and Buildings, Volume 151, Pages 332-343, ISSN 0378-7788, https://doi.org/10.1016/j.enbuild.2017.06.07610.1016/j.enbuild.2017.06.076 Search in Google Scholar

[23] Seyedzadeh, S., Rahimian, F., Glesk, I., & Roper, M. (2018). Machine learning for estimation of building energy consumption and performance: a review. Visualization In Engineering, 6(1). https://doi.org/10.1186/s40327-018-0064-710.1186/s40327-018-0064-7 Search in Google Scholar

[24] Culcea, M. (2015), Modelarea sistemelor pervasive utilizând elemente de inteligență artificială în vederea implementării lor în conducerea automată a proceselor din instalații, PhD Thesis, Technical University of Civil Engineering, Bucharest, Romania. Search in Google Scholar

[25] Baze de practică în UTCB pentru domeniul construcţiilor inteligente, https://utcb.ro/cercetare/fondul-dedezvoltare-institutionala/d3-asigurarea-functionarii-infrastructurilor-de-sustinere-a-activitatilor-didactice/ Search in Google Scholar

[26] Hangli Ge, Zhe Sun, Yasuhira Chiba, Noboru Koshizuka (2022). Accurate indoor location awareness based on machine learning of environmental sensing data, Computers & Electrical Engineering, Volume 98, 107676, ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2021.10767610.1016/j.compeleceng.2021.107676 Search in Google Scholar

[27] Jiang, F., Ma, L., Broyd, T., & Chen, K. (2021). Digital twin and its implementations in the civil engineering sector. Automation In Construction, 130, 103838. https://doi.org/10.1016/j.autcon.2021.10383810.1016/j.autcon.2021.103838 Search in Google Scholar

[28] Pereira, V., Santos, J., Leite, F., & Escórcio, P. (2021). Using BIM to improve building energy efficiency – A scientometric and systematic review. Energy And Buildings, 250, 111292. https://doi.org/10.1016/j.enbuild.2021.11129210.1016/j.enbuild.2021.111292 Search in Google Scholar

[29] Oancea Cristian, Caluianu Sorin, (2013). Designing intelligent buildings for people’s well-being using an artificial intelligence approach, Chapter 6 in Intelligent Buildings, Design, management and operation, Second Edition, Editor: Derek Clements-Croome, ICE Publishing, Londra, ISBN 978-0-7277-5734-0 doi: 10.1680/ib.57340.089, http://www.icevirtuallibrary.com/content/book/102869 Search in Google Scholar

[30] Oancea Cristian, (2012). Contributions to the implementation of the artificial intelligence for the determination of the global comfort in the intelligent buildings. PhD Thesis, Technical University of Civil Engineering, Bucharest, Romania. Search in Google Scholar

[31] Caluianu Sorin (2000). Inteligenta artificiala in instalatii – logica fuzzy si teoria posibilitatilor, MATRIX ROM, Bucuresti, 2000, ISBN 973-685-120-6. Search in Google Scholar

[32] Warwich Kevin (2012). Artificial Intelligence, the basics, Routledge, ISBN 978-0-415-56483-0. Search in Google Scholar

[33] Debrah, C., Chan, A., & Darko, A. (2022). Artificial intelligence in green building. Automation In Construction, 137, 104192. https://doi.org/10.1016/j.autcon.2022.10419210.1016/j.autcon.2022.104192 Search in Google Scholar

[34] Energy performance of buildings directive. European Commission website (2022). Retrieved 4 June 2022, from https://energy.ec.europa.eu/topics/energy-efficiency/energy-efficient-buildings/energy-performance-buildings-directive_en Search in Google Scholar

[35] Alanne, K., & Sierla, S. (2022). An overview of machine learning applications for smart buildings. Sustainable Cities and Society, 76, 103445. https://doi.org/10.1016/j.scs.2021.10344510.1016/j.scs.2021.103445 Search in Google Scholar

[36] Woo, J., Fatima, R., Kibert, C., Newman, R., Tian, Y., & Srinivasan, R. (2021). Applying blockchain technology for building energy performance measurement, reporting, and verification (MRV) and the carbon credit market: A review of the literature. Building And Environment, 205, 108199. https://doi.org/10.1016/j.buildenv.2021.10819910.1016/j.buildenv.2021.108199 Search in Google Scholar

eISSN:
2784-1391
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
4 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Technik, Einführungen und Gesamtdarstellungen, andere