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 02007
Number of page(s) 6
Section Ventilation and Airflow in Buildings
DOI https://doi.org/10.1051/e3sconf/202339602007
Published online 16 June 2023
  1. S.A. Kalogirou, Artificial neural networks in renewable energy systems applications: a review. Renewable Sustainable Energy Rev 5.4, 373-401, (2001) [CrossRef] [Google Scholar]
  2. S.A. Kalogirou, Artificial neural networks in energy applications in buildings, Int. J. Low Carbon Technol 1.3, 201-216, (2006) [CrossRef] [Google Scholar]
  3. S.A. Kalogirou, M. Bojic, Artificial neural networks for the prediction of the energy consumption of a passive solar building, Energy 25.5, 479-491, (2000) [CrossRef] [Google Scholar]
  4. B.B. Ekici, U.T. Aksoy, Prediction of building energy consumption by using artificial neural networks, Adv. Eng. Softw 40.5, 356-362, (2009) [CrossRef] [Google Scholar]
  5. S.A. Kalogirou, S. Panteliou, A. Dentsoras, Modeling of solar domestic water heating systems using artificial neural networks, Sol Energy 65.6, 335-342, (1999) [CrossRef] [Google Scholar]
  6. M. M. Gouda, S. Danaher, C. P. Underwood, Application of an artificial neural network for modelling the thermal dynamics of a building’s space and its heating system, Math Comput Model Dyn Syst 8.3, 333-344 (2002) [Google Scholar]
  7. H.X. Zhao, F. Magoulès, A review on the prediction of building energy consumption, Renewable Sustainable Energy Rev 16.6, 3586-3592 (2012) [CrossRef] [Google Scholar]
  8. Y. Chen, L.K. Norford, H.W. Samuelson, A. Malkawi, Optimal control of HVAC and window systems for natural ventilation through reinforcement learning, Energy Build 169, 195-205 (2018) [CrossRef] [Google Scholar]
  9. X. Dai, J. Liu, X. Zhang, W. Chen, An artificial neural network model using outdoor environmental parameters and residential building characteristics for predicting the nighttime natural ventilation effect, Build Environ 159, 106139 (2019) [CrossRef] [Google Scholar]
  10. Q. Zhou, R. Ooka, Influence of data preprocessing on neural network performance for reproducing CFD simulations of non-isothermal indoor airflow distribution. Energy Build 230, 110525 (2021) [CrossRef] [Google Scholar]
  11. G.M. Stavrakakis, P.L. Zervas, H. Sarimveis, N.C. Markatos, Optimization of window-openings design for thermal comfort in naturally ventilated buildings. Appl. Math. Model 36.1, 193-211, (2012) [CrossRef] [Google Scholar]
  12. P.W. Tien, S. Wei, T. Liu, J. Calautit, J. Darkwa, and C. Wood, A Deep Learning Approach Towards the Detection and Recognition of Opening of Windows for Effective Management of Building Ventilation Heat Losses and Reducing Space Heating Demand, Renew. Energy 177, 603-625 ,(2021) [CrossRef] [Google Scholar]
  13. K. Hiyama, Y. Omodaka, Operation of climate-adaptive building shells utilizing machine learning under sparse data conditions. Journal of Building Engineering, 43 (2021): 103027. [CrossRef] [Google Scholar]
  14. T. Srisamranrungruang, K. Hiyama, Application of artificial neural network for natural ventilation schemes to control operable windows. Heliyon. 2022 Nov 22:e11817. [CrossRef] [Google Scholar]
  15. SHASE, Guideline of Test Procedure for the Evaluation of Building Energy Simulation Tool, The Society of Heating, Air-Conditioning and Sanitary Engineers of Japan, 2016. (in Japanese). [Google Scholar]
  16. M. Indraganti, R. Ooka, HB. Rijal, Thermal comfort and acceptability in offices in Japan and India: a comparative analysis, 2014 AKITA Technical Papers of Annual Meeting, The Society of Heating, Air-Conditioning and Sanitary Engineers of Japan 2014 6 17-20, (2014) [Google Scholar]

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