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Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture
Joint Authors
Wu, Hao
Yang, Hang
Ke, Ji
Qin, Yude
Wang, Biao
Yang, Shundong
Zhao, Xing
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-20, 20 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-15
Country of Publication
Egypt
No. of Pages
20
Main Subjects
Information Technology and Computer Science
Abstract EN
Model predictive control is theoretically suitable for optimal control of the building, which provides a framework for optimizing a given cost function (e.g., energy consumption) subject to constraints (e.g., thermal comfort violations and HVAC system limitations) over the prediction horizon.
However, due to the buildings’ heterogeneous nature, control-oriented physical models’ development may be cost and time prohibitive.
Data-driven predictive control, integration of the “Internet of Things”, provides an attempt to bypass the need for physical modeling.
This work presents an innovative study on a data-driven predictive control (DPC) for building energy management under the four-tier building energy Internet of Things architecture.
Here, we develop a cloud-based SCADA building energy management system framework for the standardization of communication protocols and data formats, which is favorable for advanced control strategies implementation.
Two DPC strategies based on building predictive models using the regression tree (RT) and the least-squares boosting (LSBoost) algorithms are presented, which are highly interpretable and easy for different stakeholders (end-user, building energy manager, and/or operator) to operate.
The predictive model’s complexity is reduced by efficient feature selection to decrease the variables’ dimensionality and further alleviate the DPC optimization problem’s complexity.
The selection is dependent on the principal component analysis (PCA) and the importance of disturbance variables (IoD).
The proposed strategies are demonstrated both in residential and office buildings.
The results show that the DPC-LSBoost has outperformed the DPC-RT and other existing control strategies (MPC, TDNN) in performance, scalability, and robustness.
American Psychological Association (APA)
Ke, Ji& Qin, Yude& Wang, Biao& Yang, Shundong& Wu, Hao& Yang, Hang…[et al.]. 2020. Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1214721
Modern Language Association (MLA)
Ke, Ji…[et al.]. Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-20.
https://search.emarefa.net/detail/BIM-1214721
American Medical Association (AMA)
Ke, Ji& Qin, Yude& Wang, Biao& Yang, Shundong& Wu, Hao& Yang, Hang…[et al.]. Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1214721
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214721