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All issues Volume 396 (2023) E3S Web of Conf., 396 (2023) 01101 References
Open Access
Issue
E3S Web of Conf.
Volume 396, 2023
The 11th International Conference on Indoor Air Quality, Ventilation & Energy Conservation in Buildings (IAQVEC2023)
Article Number 01101
Number of page(s) 8
Section Indoor Environmental Quality (IEQ), Human Health, Comfort and Productivity
DOI https://doi.org/10.1051/e3sconf/202339601101
Published online 16 June 2023
  1. European Commission, “In focus: Energy efficiency in buildings ,” 17-Feb-2020. [Online]. Available: https://ec.europa.eu/info/news/focus-energy-efficiency-buildings-2020-feb-17_en. [Accessed: 07-Sep-2021]. [Google Scholar]
  2. G. Martinopoulos, K. T. Papakostas, and A. M. Papadopoulos, “A comparative review of heating systems in EU countries, based on efficiency and fuel cost,” Renew. Sustain. Energy Rev., vol. 90, pp. 687–699, Jul. 2018, doi: 10.1016/J.RSER.2018.03.060. [CrossRef] [Google Scholar]
  3. European Commission, “DIRECTIVE (EU) 2018/844 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,” Brussels, Jun. 2018. [Google Scholar]
  4. J. Al Dakheel, C. Del Pero, N. Aste, and F. Leonforte, “Smart buildings features and key performance indicators: A review,” Sustain. Cities Soc., vol. 61, p. 102328, Oct. 2020, doi: 10.1016/J.SCS.2020.102328. [CrossRef] [Google Scholar]
  5. C. Lamnatou, D. Chemisana, and C. Cristofari, “Smart grids and smart technologies in relation to photovoltaics, storage systems, buildings and the environment,” Renew. Energy, vol. 185, pp. 1376–1391, Feb. 2022, doi: 10.1016/J.RENENE.2021.11.019. [CrossRef] [Google Scholar]
  6. A. O. Windapo and A. Moghayedi, “Adoption of smart technologies and circular economy performance of buildings,” Built Environ. Proj. Asset Manag., vol. 10, no. 4, pp. 585–601, 2020, doi: 10.1108/BEPAM-04-2019-0041. [CrossRef] [Google Scholar]
  7. J. Mei and X. Xia, “Energy-efficient predictive control of indoor thermal comfort and air quality in a direct expansion air conditioning system,” Appl. Energy, vol. 195, pp. 439–452, Jun. 2017, doi: 10.1016/J.APENERGY.2017.03.076. [CrossRef] [Google Scholar]
  8. P. W. Tien, S. Wei, J. Darkwa, C. Wood, and J. K. Calautit, “Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality – A Review,” Energy AI, vol. 10, p. 100198, Nov. 2022, doi: 10.1016/J.EGYAI.2022.100198. [CrossRef] [Google Scholar]
  9. A. Kylili, P. A. Fokaides, and P. A. Lopez Jimenez, “Key Performance Indicators (KPIs) approach in buildings renovation for the sustainability of the built environment: A review,” Renewable and Sustainable Energy Reviews, vol. 56. Elsevier Ltd, pp. 906–915, 01-Apr-2016, doi: 10.1016/j.rser.2015.11.096. [CrossRef] [Google Scholar]
  10. M. Jin, S. Liu, S. Schiavon, and C. Spanos, “Automated mobile sensing: Towards high-granularity agile indoor environmental quality monitoring,” Build. Environ., vol. 127, pp. 268–276, Jan. 2018, doi: 10.1016/J.BUILDENV.2017.11.003. [CrossRef] [Google Scholar]
  11. C. Spataru, S. G.-A. E. and design, and undefined 2014, “How to monitor people ’smartly’to help reducing energy consumption in buildings?,” Taylor Fr., vol. 10, no. 1–2, pp. 60–78, Apr. 2013, doi: 10.1080/17452007.2013.837248. [Google Scholar]
  12. T. Nguyen, ; Anh, M. Aiello, A. Uk, and T. A. Nguyen, “Energy intelligent buildings based on user activity: A survey,” Elsevier, vol. 56, pp. 244–257, 2013, doi: 10.1016/j.enbuild.2012.09.005. [Google Scholar]
  13. A. Floris, S. Porcu, R. Girau, and L. Atzori, “An IoT-Based Smart Building Solution for Indoor Environment Management and Occupants Prediction,” Energies 2021, Vol. 14, Page 2959, vol. 14, no. 10, p. 2959, May 2021, doi: 10.3390/EN14102959. [Google Scholar]
  14. B. Dong, V. Prakash, F. Feng, and Z. O’Neill, “A review of smart building sensing system for better indoor environment control,” Energy Build., vol. 199, pp. 29–46, Sep. 2019, doi: 10.1016/J.ENBUILD.2019.06.025. [CrossRef] [Google Scholar]
  15. C. C. Cheng and D. Lee, “Smart sensors enable smart air conditioning control,” Sensors (Switzerland), vol. 14, no. 6, pp. 11179–11203, Jun. 2014, doi: 10.3390/S140611179. [CrossRef] [Google Scholar]
  16. Y. Bae et al., “Sensor impacts on building and HVAC controls: A critical review for building energy performance,” Adv. Appl. Energy, vol. 4, p. 100068, Nov. 2021, doi: 10.1016/J.ADAPEN.2021.100068. [CrossRef] [Google Scholar]
  17. S. Lee, J. Joe, P. Karava, I. Bilionis, and A. Tzempelikos, “Implementation of a self-tuned HVAC controller to satisfy occupant thermal preferences and optimize energy use,” Energy Build., vol. 194, pp. 301–316, Jul. 2019, doi: 10.1016/J.ENBUILD.2019.04.016. [CrossRef] [Google Scholar]
  18. H. Li, D. Yu, and J. E. Braun, “A review of virtual sensing technology and application in building systems,” HVAC R Res., vol. 17, no. 5, pp. 619–645, 2011, doi: 10.1080/10789669.2011.573051. [Google Scholar]
  19. V. Reppa, P. Papadopoulos, M. M. Polycarpou, and C. G. Panayiotou, “A distributed virtual sensor scheme for smart buildings based on adaptive approximation,” Proc. Int. Jt. Conf. Neural Networks, pp. 99–106, Sep. 2014, doi: 10.1109/IJCNN.2014.6889976. [Google Scholar]
  20. Y. Zhao, W. Zeiler, G. Boxem, and T. Labeodan, “Virtual occupancy sensors for real-time occupancy information in buildings,” Build. Environ., vol. 93, no. P2, pp. 9–20, Nov. 2015, doi: 10.1016/J.BUILDENV.2015.06.019. [CrossRef] [Google Scholar]
  21. T. H. Pedersen, K. U. Nielsen, and S. Petersen, “Method for room occupancy detection based on trajectory of indoor climate sensor data,” Build. Environ., vol. 115, pp. 147–156, Apr. 2017, doi: 10.1016/J.BUILDENV.2017.01.023. [CrossRef] [Google Scholar]
  22. T. Peffer, M. Pritoni, A. Meier, C. Aragon, and D. Perry, “How people use thermostats in homes: A review,” Build. Environ., vol. 46, no. 12, pp. 2529–2541, 2011, doi: 10.1016/J.BUILDENV.2011.06.002. [CrossRef] [Google Scholar]
  23. Z. Pang, Y. Chen, J. Zhang, Z. O’Neill, H. Cheng, and B. Dong, “How much HVAC energy could be saved from the occupant-centric smart home thermostat: A nationwide simulation study,” Appl. Energy, vol. 283, p. 116251, Feb. 2021, doi: 10.1016/J.APENERGY.2020.116251. [CrossRef] [Google Scholar]
  24. M. Kong, B. Dong, R. Zhang, and Z. O’Neill, “HVAC energy savings, thermal comfort and air quality for occupant-centric control through a side-by-side experimental study,” Appl. Energy, vol. 306, p. 117987, Jan. 2022, doi: 10.1016/J.APENERGY.2021.117987. [CrossRef] [Google Scholar]
  25. A. I. Dounis and C. Caraiscos, “Advanced control systems engineering for energy and comfort management in a building environment—A review,” Renew. Sustain. Energy Rev., vol. 13, no. 6–7, pp. 1246–1261, Aug. 2009, doi: 10.1016/J.RSER.2008.09.015. [CrossRef] [Google Scholar]
  26. Y. Song, S. Wu, and Y. Y. Yan, “Control strategies for indoor environment quality and energy efficiency-a review,” Int. J. Low-Carbon Technol., vol. 10, no. 3, pp. 305–312, Jul. 2013, doi: 10.1093/IJLCT/CTT051. [Google Scholar]
  27. H. Yan, Y. Pan, Z. Li, and S. Deng, “Further development of a thermal comfort based fuzzy logic controller for a direct expansion air conditioning system,” Appl. Energy, vol. 219, pp. 312–324, Jun. 2018, doi: 10.1016/j.apenergy.2018.03.045. [CrossRef] [Google Scholar]
  28. C. v. Altrock, H. O. Arend, B. Krause, C. Steffens, and E. Behrens-Römmler, “Adaptive fuzzy control applied to home heating system,” Fuzzy Sets Syst., vol. 61, no. 1, pp. 29–35, Jan. 1994, doi: 10.1016/0165-0114(94)90281-X. [CrossRef] [Google Scholar]
  29. F. Calvino, M. La Gennusa, G. Rizzo, and G. Scaccianoce, “The control of indoor thermal comfort conditions: Introducing a fuzzy adaptive controller,” Energy Build., vol. 36, no. 2, pp. 97–102, Feb. 2004, doi: 10.1016/J.ENBUILD.2003.10.004. [CrossRef] [Google Scholar]
  30. I. Gancliev, A. Taueva, K. Kutryanski, and M. Petrov, “Decoupling Fuzzy-Neural Temperature and Humidity Control in HVAC Systems,” IFAC-PapersOnLine, vol. 52, no. 25, pp. 299–304, Jan. 2019, doi: 10.1016/J.IFACOL.2019.12.539. [Google Scholar]
  31. J. X. Xu, C. C. Hang, and C. Liu, “Parallel structure and tuning of a fuzzy PID controller,” Automatica, vol. 36, no. 5, pp. 673–684, 2000, doi: 10.1016/S0005-1098(99)00192-2. [CrossRef] [Google Scholar]
  32. A. Goel, A. K. Goel, and A. Kumar, “The role of artificial neural network and machine learning in utilizing spatial information,” Spat. Inf. Res., vol. 1, pp. 1–11, Nov. 2022, doi: 10.1007/S41324-022-00494-X/TABLES/3. [Google Scholar]
  33. A. Sözen, M. A. Akçayol, and E. Arcakliolu, “Forecasting net energy consumption using artificial neural network,” Energy Sources, Part B Econ. Plan. Policy, vol. 1, no. 2, pp. 147–155, Jul. 2006, doi: 10.1080/009083190881562. [Google Scholar]
  34. A. Alam, C. Baek, H. H.-A. mechanics and materials, and undefined 2016, “Prediction and analysis of building energy efficiency using artificial neural network and design of experiments,” Trans Tech Publ, vol. 37, pp. 37–41, 2014. [Google Scholar]
  35. R. Ilambirai, P. Sivasankari, … S. P.-A. C., and undefined 2019, “Efficient self-learning artificial neural network controller for critical heating, ventilation and air conditioning systems,” aip.scitation.org, vol. 2112, p. 20103, Jun. 2019, doi: 10.1063/1.5112348. [Google Scholar]
  36. D. Palladino, I. Nardi, C. B.- Energies, and undefined 2020, “Artificial neural network for the thermal comfort index prediction: Development of a new simplified algorithm,” mdpi.com, doi: 10.3390/en13174500. [Google Scholar]
  37. O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. E. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey,” Heliyon, vol. 4, no. 11, p. e00938, Nov. 2018, doi: 10.1016/J.HELIYON.2018.E00938. [CrossRef] [PubMed] [Google Scholar]
  38. D. Hsu, “Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data,” Appl. Energy, vol. 160, pp. 153–163, Dec. 2015, doi: 10.1016/J.APENERGY.2015.08.126. [CrossRef] [Google Scholar]
  39. S. J. Cao and C. Ren, “Ventilation control strategy using low-dimensional linear ventilation models and artificial neural network,” Build. Environ., vol. 144, pp. 316–333, Oct. 2018, doi: 10.1016/J.BUILDENV.2018.08.032. [CrossRef] [Google Scholar]
  40. J. von Grabe, “Potential of artificial neural networks to predict thermal sensation votes,” Appl. Energy, vol. 161, pp. 412–424, Jan. 2016, doi: 10.1016/J.APENERGY.2015.10.061. [CrossRef] [Google Scholar]
  41. Z. Deng and Q. Chen, “Simulating the impact of occupant behavior on energy use of HVAC systems by implementing a behavioral artificial neural network model,” Energy Build., vol. 198, pp. 216–227, Sep. 2019, doi: 10.1016/J.ENBUILD.2019.06.015. [CrossRef] [Google Scholar]
  42. M. K. Kim, B. Cremers, J. Liu, J. Zhang, and J. Wang, “Prediction and correlation analysis of ventilation performance in a residential building using artificial neural network models based on data-driven analysis,” Sustain. Cities Soc., vol. 83, p. 103981, Aug. 2022, doi: 10.1016/J.SCS.2022.103981. [CrossRef] [Google Scholar]
  43. A. Mirakhorli and B. Dong, “Occupancy behavior based model predictive control for building indoor climate—A critical review,” Energy Build., vol. 129, pp. 499–513, Oct. 2016, doi: 10.1016/J.ENBUILD.2016.07.036. [CrossRef] [Google Scholar]
  44. I. Hazyuk, C. Ghiaus, and D. Penhouet, “Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part I – Building modeling,” Build. Environ., vol. 51, pp. 379–387, May 2012, doi: 10.1016/J.BUILDENV.2011.11.009. [CrossRef] [Google Scholar]
  45. R. Sangi, A. Kümpel, and D. Müller, “Real-life implementation of a linear model predictive control in a building energy system,” J. Build. Eng., vol. 22, pp. 451–463, Mar. 2019, doi: 10.1016/J.JOBE.2019.01.002. [CrossRef] [Google Scholar]
  46. X. Xu, B. Fu, Z. Wu, and G. Sun, “Predictive control for indoor environment based on thermal adaptation,” Sci. Prog., vol. 104, no. 2, 2021, doi: 10.1177/00368504211006971. [Google Scholar]
  47. G. Serale, M. Fiorentini, A. Capozzoli, D. Bernardini, and A. Bemporad, “Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities,” Energies, vol. 11, no. 3, p. 631, Mar. 2018, doi: 10.3390/EN11030631. [CrossRef] [Google Scholar]
  48. S. R. West, J. K. Ward, and J. Wall, “Trial results from a model predictive control and optimisation system for commercial building HVAC,” Energy Build., vol. 72, pp. 271–279, Apr. 2014, doi: 10.1016/J.ENBUILD.2013.12.037. [CrossRef] [Google Scholar]
  49. M. Gholamzadehmir, C. Del Pero, S. Buffa, R. Fedrizzi, and N. Aste, “Adaptive-predictive control strategy for HVAC systems in smart buildings – A review,” Sustain. Cities Soc., vol. 63, p. 102480, Dec. 2020, doi: 10.1016/J.SCS.2020.102480. [CrossRef] [Google Scholar]
  50. A. Javed, H. Larijani, A. Ahmadinia, R. Emmanuel, M. Mannion, and D. Gibson, “Design and implementation of a cloud enabled random neural network-based decentralized smart controller with intelligent sensor nodes for HVAC,” IEEE Internet Things J., vol. 4, no. 2, pp. 393–403, 2017. [CrossRef] [Google Scholar]
  51. N. Aste, M. Manfren, and G. Marenzi, “Building Automation and Control Systems and performance optimization: A framework for analysis,” Renew. Sustain. Energy Rev., vol. 75, pp. 313–330, Aug. 2017, doi: 10.1016/J.RSER.2016.10.072. [CrossRef] [Google Scholar]
  52. M. Schmelas, T. Feldmann, P. Wellnitz, and E. Bollin, “Adaptive predictive control of thermo-active building systems (TABS) based on a multiple regression algorithm: First practical test,” Energy Build., vol. 129, pp. 367–377, Oct. 2016, doi: 10.1016/J.ENBUILD.2016.08.013. [CrossRef] [Google Scholar]
  53. J. K. Day et al., “A review of select human-building interfaces and their relationship to human behavior, energy use and occupant comfort,” Build. Environ., vol. 178, p. 106920, Jul. 2020, doi: 10.1016/J.BUILDENV.2020.106920. [CrossRef] [Google Scholar]
  54. J. K. Day and D. E. Gunderson, “Understanding high performance buildings: The link between occupant knowledge of passive design systems, corresponding behaviors, occupant comfort and environmental satisfaction,” Build. Environ., vol. 84, pp. 114–124, Jan. 2015, doi: 10.1016/J.BUILDENV.2014.11.003. [CrossRef] [Google Scholar]
  55. D. Kolokotsa, K. Kalaitzakis, E. Antonidakis, and G. S. Stavrakakis, “Interconnecting smart card system with PLC controller in a local operating network to form a distributed energy management and control system for buildings,” Energy Convers. Manag., vol. 43, no. 1, pp. 119–134, Jan. 2002, doi: 10.1016/S0196-8904(01)00013-9. [CrossRef] [Google Scholar]
  56. X. Liu, S. Lee, I. Bilionis, P. Karava, J. Joe, and S. A. Sadeghi, “A user-interactive system for smart thermal environment control in office buildings,” Appl. Energy, vol. 298, p. 117005, Sep. 2021, doi: 10.1016/J.APENERGY.2021.117005. [CrossRef] [Google Scholar]
  57. W. Zeiler, R. Van Houten, and G. Boxem, “SMART buildings: Intelligent software agents building occupants leading the energy systems,” Sustain. Energy Build. - Proc. Int. Conf. Sustain. Energy Build. SEB’09, pp. 9–17, 2009. [Google Scholar]
  58. M. Nelke and C. Håkansson, Competitive intelligence for information professionals. Chandos Publishing, 2015. [Google Scholar]
  59. I. Jefferson, D. V. L. Hunt, C. A. Birchall, and C. D. F. Rogers, “Sustainability indicators for environmental geotechnics,” Proc. Inst. Civ. Eng. Eng. Sustain., vol. 160, no. 2, pp. 57–78, 2007, doi: 10.1680/ensu.2007.160.2.57. [CrossRef] [Google Scholar]
  60. S. Brown, “HIGH QUALITY INDOOR ENVIRONMENTS FOR OFFICE BUILDINGS,” in Clients Driving Innovation: Moving Ideas into Practice (12-14 March 2006) 1 Cooperative Research Centre (CRC) for Construction Innovation, 2006. [Google Scholar]
  61. E. Azar, C. Nikolopoulou, and S. Papadopoulos, “Integrating and optimizing metrics of sustainable building performance using human-focused agent-based modeling,” Appl. Energy, vol. 183, pp. 926–937, Dec. 2016, doi: 10.1016/J.APENERGY.2016.09.022. [CrossRef] [Google Scholar]
  62. A. Nabil and J. Mardaljevic, “Useful daylight illuminance: A new paradigm for assessing daylight in buildings,” Light. Res. Technol., vol. 37, no. 1, pp. 41–59, 2005, doi: 10.1191/1365782805LI128OA. [CrossRef] [Google Scholar]
  63. I. Konstantzos, A. Tzempelikos, and Y. C. Chan, “Experimental and simulation analysis of daylight glare probability in offices with dynamic window shades,” Build. Environ., vol. 87, pp. 244–254, May 2015, doi: 10.1016/J.BUILDENV.2015.02.007. [CrossRef] [Google Scholar]
  64. T. Kazanasmaz, L. O. Grobe, C. Bauer, M. Krehel, and S. Wittkopf, “Three approaches to optimize optical properties and size of a South-facing window for spatial Daylight Autonomy,” Build. Environ., vol. 102, pp. 243–256, Jun. 2016, doi: 10.1016/J.BUILDENV.2016.03.018. [CrossRef] [Google Scholar]
  65. S. Carlucci and L. Pagliano, “A review of indices for the long-term evaluation of the general thermal comfort conditions in buildings,” Energy Build., vol. 53, pp. 194–205, Oct. 2012, doi: 10.1016/J.ENBUILD.2012.06.015. [CrossRef] [Google Scholar]
  66. N. Djongyang, R. Tchinda, and D. Njomo, “Thermal comfort: A review paper,” Renew. Sustain. Energy Rev., vol. 14, no. 9, pp. 2626–2640, Dec. 2010, doi: 10.1016/J.RSER.2010.07.040. [CrossRef] [Google Scholar]
  67. A. Fratean and P. Dobra, “Key performance indicators for the evaluation of building indoor air temperature control in a context of demand side management: An extensive analysis for Romania,” Sustain. Cities Soc., vol. 68, p. 102805, May 2021, doi: 10.1016/J.SCS.2021.102805. [CrossRef] [Google Scholar]
  68. W. L. Cheng, Y. S. Chen, J. Zhang, T. J. Lyons, J. L. Pai, and S. H. Chang, “Comparison of the Revised Air Quality Index with the PSI and AQI indices,” Sci. Total Environ., vol. 382, no. 2–3, pp. 191–198, Sep. 2007, doi: 10.1016/J.SCITOTENV.2007.04.036. [CrossRef] [Google Scholar]
  69. R. J. Cureau et al., “Bridging the gap from test rooms to field-tests for human indoor comfort studies: A critical review of the sustainability potential of living laboratories,” Energy Res. Soc. Sci., vol. 92, p. 102778, Oct. 2022, doi: 10.1016/J.ERSS.2022.102778. [CrossRef] [Google Scholar]
  70. Z. Wang, R. Matsuhashi, and H. Onodera, “Towards wearable thermal comfort assessment framework by analysis of heart rate variability,” Build. Environ., vol. 223, p. 109504, Sep. 2022, doi: 10.1016/J.BUILDENV.2022.109504. [CrossRef] [Google Scholar]
  71. N. D. Dahlan and Y. Y. Gital, “Thermal sensations and comfort investigations in transient conditions in tropical office,” Appl. Ergon., vol. 54, pp. 169–176, May 2016, doi: 10.1016/J.APERGO.2015.12.008. [CrossRef] [Google Scholar]
  72. P. Antoniadou and A. M. Papadopoulos, “Occupants’ thermal comfort: State of the art and the prospects of personalized assessment in office buildings,” Energy Build., vol. 153, pp. 136–149, Oct. 2017, doi: 10.1016/J.ENBUILD.2017.08.001. [CrossRef] [Google Scholar]

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