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Gómez-Chaparro, M, García-Sanz-Calcedo, J and Aunión-Villa, J (2020) Maintenance in hospitals with less than 200 beds: efficiency indicators. Building Research & Information, 48(05), 526–37.

Gao, S, Jin, R and Lu, W (2020) Design for manufacture and assembly in construction: a review. Building Research & Information, 48(05), 538–50.

Gao, X and Pishdad-Bozorgi, P (2020) A framework of developing machine learning models for facility life-cycle cost analysis. Building Research & Information, 48(05), 501–25.

Glew, D, Johnston, D, Miles-Shenton, D and Thomas, F (2020) Retrofitting suspended timber ground-floors; comparing aggregated and disaggregated evaluation methods. Building Research & Information, 48(05), 572–86.

Nardecchia, F, Mattoni, B, Burattini, C and Bisegna, F (2020) The impact of humidity on vortex creation around isolated buildings. Building Research & Information, 48(05), 551–71.

Patel, H and Green, S D (2020) Beyond the performance gap: reclaiming building appraisal through archival research. Building Research & Information, 48(05), 469–84.

Pereira, P F, Ramos, N M M and Simões, M L (2020) Data-driven occupant actions prediction to achieve an intelligent building. Building Research & Information, 48(05), 485–500.

  • Type: Journal Article
  • Keywords: Occupant behaviour; intelligent buildings; data mining; environmental monitoring; machine learning; logistic regressions;
  • ISBN/ISSN: 0961-3218
  • URL: https://doi.org/10.1080/09613218.2019.1692648
  • Abstract:
    An intelligent building has to know the specificities of the occupants and determine their drivers to perform actions so that it can optimize the building operation. Five windows of different rooms of the same dwelling were analysed in-depth to understand the specificities and variations of occupants’ behaviour. Logistic regressions were used as a machine learning method to predict occupants’ actions. The windows opening prediction models were formulated by taking into account continuous and categorical variables. An evaluation of the required data length that allows obtaining the prediction models with results identical to those obtained with the complete year was performed. It was concluded that the best option was to use at least 15 days in summer and 15 days in winter to have a reliable prediction for the full year. The model constructed for each window did not show good prediction success when applied in another room of the same dwelling. This study shows that the specificity of humans needs do not allow a generalization of their behaviours in the built environment. Thus, it is necessary to adapt the algorithms of the building automation systems through data-driven machine learning techniques.