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Volume 6 Issue 6
Nov.  2019

IEEE/CAA Journal of Automatica Sinica

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Abdulaziz Almalaq, Jun Hao, Jun Jason Zhang and Fei-Yue Wang, "Parallel Building: A Complex System Approach for Smart Building Energy Management," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1452-1461, Nov. 2019. doi: 10.1109/JAS.2019.1911768
Citation: Abdulaziz Almalaq, Jun Hao, Jun Jason Zhang and Fei-Yue Wang, "Parallel Building: A Complex System Approach for Smart Building Energy Management," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1452-1461, Nov. 2019. doi: 10.1109/JAS.2019.1911768

Parallel Building: A Complex System Approach for Smart Building Energy Management

doi: 10.1109/JAS.2019.1911768
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  • These days’ smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5G, the ACP theory (i.e., artificial systems, computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.


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    • The ACP theory enhanced the modeling of complex systems such as buildings.
    • Multivariate time series (MTS) problem can be modeled with sequential deep learning (DL) methods.
    • The objective is to model a DL energy consumption prediction using MTS.
    • The MTS models improved the prediction accuracy in complex systems such as buildings.
    • The proposed framework can be applied to many other smart environment problems.


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