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Agee, P, Gao, X, Paige, F, McCoy, A and Kleiner, B (2021) A human-centred approach to smart housing. Building Research & Information, 49(01), 84–99.

Erişen, S (2021) Incremental transformation of spatial intelligence from smart systems to sensorial infrastructures. Building Research & Information, 49(01), 113–26.

Fettermann, D C, Borriello, A, Pellegrini, A, Cavalcante, C G, Rose, J M and Burke, P F (2021) Getting smarter about household energy: the who and what of demand for smart meters. Building Research & Information, 49(01), 100–12.

Fu, Y and Zhu, J (2021) Trusted data infrastructure for smart cities: a blockchain perspective. Building Research & Information, 49(01), 21–37.

Ihm, S, Lee, H, Lee, E and Park, Y (2021) A policy knowledge- and reasoning-based method for data-analytic city policymaking. Building Research & Information, 49(01), 38–54.

Kang, L, Liu, S, Zhang, H and Gong, D (2021) Person anomaly detection-based videos surveillance system in urban integrated pipe gallery. Building Research & Information, 49(01), 55–68.

Lee, Y L and Lee, Y (2021) Developing an autonomous psychological behaviour of virtual user to atypical architectural geometry. Building Research & Information, 49(01), 69–83.

Nuhu, B K, Aliyu, I, Adegboye, M A, Ryu, J K, Olaniyi, O M and Lim, C G (2021) Distributed network-based structural health monitoring expert system. Building Research & Information, 49(01), 144–59.

V E, S, Shin, C and Cho, Y (2021) Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city. Building Research & Information, 49(01), 127–43.

  • Type: Journal Article
  • Keywords: Data mining; energy consumption; feature ranking; data analysis;
  • ISBN/ISSN: 0961-3218
  • URL: https://doi.org/10.1080/09613218.2020.1809983
  • Abstract:
    The fast development of urban advancement in the past decade requires reasonable and realistic solutions for transport, building infrastructure, natural conditions, and personal satisfaction in smart cities. This paper presents and explores predictive energy consumption models based on data-mining techniques for a smart small-scale steel industry in South Korea. Energy consumption data is collected using IoT based systems and used for prediction. Data used include the lagging and leading current reactive power, the lagging and leading current power factor, carbon dioxide emissions, and load types. Five statistical algorithms are used for energy consumption prediction:(a) General linear regression, (b) Classification and regression trees, (c) Support vector machine with a radial basis kernel, (d) K nearest neighbours, (e) CUBIST. Root mean squared error, Mean absolute error and Coefficient of variation are used to measure the prediction efficiency of the models. The results show that CUBIST model provides best results with lower error values and this model can be used for the development of energy efficient structural design which helps to optimize the energy consumption and policy making in smart cities.

Valks, B, Arkesteijn, M H, Koutamanis, A and den Heijer, A C (2021) Towards a smart campus: supporting campus decisions with Internet of Things applications. Building Research & Information, 49(01), 1–20.