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

Abdulaziz Almalaq; Jun Hao; Jun Jason Zhang; Fei-Yue Wang

doi:10.1109/JAS.2019.1911768

Volume 6 Issue 6

Nov. 2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(6): 1452-1461

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

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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

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

doi: 10.1109/JAS.2019.1911768

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Author Bio:
Abdulaziz Almalaq (S’14–M’19) received the B.S. degree in electrical engineering from the engineering college, University of Hail, Hail, Saudi Arabia, in 2011, the M.S. and Ph.D. degrees in electrical engineering from the Department of Electrical and Computer Engineering, University of Denver, Denver, Colorado, USA, in 2015 and 2019. He is currently an Assistant Professor in the Department of Electrical Engineering, University of Hail. His research interests include modeling smart buildings, smart grids, smart cities, and smart energy systems using artificial intelligence, machine learning, deep learning, and quantum computing

Jun Hao (S’18) is currently a Ph.D. candidate at University of Denver, majoring in electric power engineering. He received the M.S. degree in electrical and computer engineering, specializing in electric power engineering from University of Denver, Colorado, USA, in 2015. He dedicates himself to the research of electric power engineering, and artificial intelligence

Jun Jason Zhang (S’06–M’09–SM’14) received the B.E. and M.E. degrees in electrical engineering from Huazhong University of Science and Technology, Wuhan, China, in 2003 and 2005, respectively, and the Ph.D. degree in electrical engineering from Arizona State University, USA, in 2008. He is currently a Professor of electrical and computer engineering at Wuhan University. He authored/ coauthored over 70 peer reviewed publications and he is the Technical Co-Chair for the 48th North American Power Symposium (NAPS 2016). His research interests include sensing theory, signal processing and implementation, time-varying system modeling, and their applications in intelligent power and energy systems

Fei-Yue Wang (S’87–M’89–SM’94–F’03) received his Ph.D. in Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, New York in 1990. He joined the University of Arizona in 1990 and became a Professor and Director of the Robotics and Automation Laboratory (RAL) and Program in Advanced Research for Complex Systems (PARCS). In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, under the support of the Outstanding Oversea Chinese Talents Program from the State Planning Council and “100 Talent Program” from CAS, and in 2002, was appointed as the Director of the Key Laboratory of Complex Systems and Intelligence Science, CAS. In 2011, he became the State Specially Appointed Expert and the Director of the State Key Laboratory of Management and Control for Complex Systems. Dr. Wang’s current research focuses on methods and applications for parallel systems, social computing, and knowledge automation. He was the Founding Editorin-Chief of the International Journal of Intelligent Control and Systems (1995–2000), Founding EiC of IEEE ITS Magazine (2006–2007), EiC of IEEE Intelligent Systems (2009–2012), and EiC of IEEE Transactions on ITS (2009–2016). Currently he is EiC of China’s Journal of Command and Control. Since 1997, he has served as General or Program Chair of more than 20 IEEE, INFORMS, ACM, ASME conferences. He was the President of IEEE ITS Society (2005–2007), Chinese Association for Science and Technology (CAST, USA) in 2005, the American Zhu Kezhen Education Foundation (2007–2008), and the Vice President of the ACM China Council (2010–2011). Since 2008, he has been the Vice President and Secretary General of Chinese Association of Automation. Dr. Wang is an elected Fellow of IEEE, INCOSE, IFAC, ASME, and AAAS. In 2007, he received the 2nd Class National Prize in Natural Sciences of China and awarded the Outstanding Scientist by ACM for his work in intelligent control and social computing. He received IEEE ITS Outstanding Application and Research Awards in 2009 and 2011, and the IEEE SMC Norbert Wiener Award in 2014
Corresponding author: A. Almalaq is with the Department of Electrical Engineering, Engineering College, University of Hail, Hail 55476, Saudi Arabia (e-mail: a.almalaq@uoh.edu.sa)
Received Date: 2018-12-24
Revised Date: 2019-02-21
Accepted Date: 2019-09-05

Available Online: 2019-09-16

Abstract

Abstract

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.
- ACP theory,
- artificial intelligence,
- data acquisition,
- deep learning (DL),
- energy consumption,
- machine learning,
- parallel energy prediction,
- prediction algorithms,
- smart grid

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Highlights

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.

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

doi: 10.1109/JAS.2019.1911768

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