Short-Term Load Forecasting Reliability in Power Plant of Cyber-Physical Energy System Considering Adaptive Denoising | IEEE Journals & Magazine | IEEE Xplore

Short-Term Load Forecasting Reliability in Power Plant of Cyber-Physical Energy System Considering Adaptive Denoising


Abstract:

Cyber-physical energy systems (CPES) are a crucial component of smart grids (SGs), and as such, they represent a specialized subset of cyber-physical systems. CPES provid...Show More

Abstract:

Cyber-physical energy systems (CPES) are a crucial component of smart grids (SGs), and as such, they represent a specialized subset of cyber-physical systems. CPES provides essential services for pricing decisions and automatic generation control through short-term load forecasting (STLF), making the accuracy of STLF critical to optimizing their operation. However, due to the numerous communication devices installed within CPES, data collection is often subject to various factors that could negatively impact load forecasting accuracy. To improve the accuracy of STLF, this article proposes a reliable method that combines an adaptive denoising technique, a 2-D deep temporal convolutional network (TDeepTCN), and a multidimensional input structure bidirectional long short-term memory-attention (MBiLSTM-attention) network. First, an adaptive approach that combines Pearson correlation coefficient and complete ensemble empirical mode decomposition with adaptive noise is utilized to effectively identify raw load series contaminated by noise and reconstruct them. Then, a TDeepTCN model is constructed using TCN to simultaneously capture and fuse both local and long-term temporal features from multiple load series. Finally, MBiLSTM-attention is employed for accurate forecasting to achieve feature processing for multidimensional depth features. Eventually, compared to existing models, our proposed model achieves the most accurate forecasting results with a mean absolute percentage error rate of only 3.98% and 4.12%, respectively, in both regions.
Published in: IEEE Systems Journal ( Volume: 17, Issue: 4, December 2023)
Page(s): 5183 - 5194
Date of Publication: 07 September 2023

ISSN Information:

Funding Agency:

Author image of Dong Ding
School of Electrical Engineering, Xi'an University of Technology, Xi'an, China
Dong Ding received the B.S. degree in software engineering and the M.S. degree in computer system architecture from Xi'an University of Technology, Xi'an, China, in 2014 and 2018, respectively, where he is currently working toward the Ph.D. degree in electrical engineering.
Dong Ding received the B.S. degree in software engineering and the M.S. degree in computer system architecture from Xi'an University of Technology, Xi'an, China, in 2014 and 2018, respectively, where he is currently working toward the Ph.D. degree in electrical engineering.View more
Author image of Junhuai Li
School of Computer Science and Engineering, Shaanxi Key Laboratory for Network Computing and Security Technology, Xi'an University of Technology, Xi'an, China
Junhuai Li received the B.S. degree in electrical automation from the Shaanxi Institute of Mechanical Engineering, Xi'an, China, in 1992, the M.S. degree in computer application technology from the Xi'an University of Technology, Xi'an, in 1999, and the Ph.D. degree in computer software and theory from Northwest University, Xi'an, in 2002.
He is currently a Professor with the School of Computer Science and Engineering, Xi'...Show More
Junhuai Li received the B.S. degree in electrical automation from the Shaanxi Institute of Mechanical Engineering, Xi'an, China, in 1992, the M.S. degree in computer application technology from the Xi'an University of Technology, Xi'an, in 1999, and the Ph.D. degree in computer software and theory from Northwest University, Xi'an, in 2002.
He is currently a Professor with the School of Computer Science and Engineering, Xi'...View more
Author image of Huaijun Wang
School of Computer Science and Engineering, Shaanxi Key Laboratory for Network Computing and Security Technology, Xi'an University of Technology, Xi'an, China
Huaijun Wang received the B.Sc. and M.Sc. degrees in computer science from the Xi'an University of Technology, Xi'an, China, in 2005 and 2010, respectively, and the Ph.D. degree in computer software and theory from Northwest University, Xi'an, China, in 2014.
He is currently a Lecturer with the Xi'an University of Technology. His research interests include application and security of CPS and modeling of effectiveness evalu...Show More
Huaijun Wang received the B.Sc. and M.Sc. degrees in computer science from the Xi'an University of Technology, Xi'an, China, in 2005 and 2010, respectively, and the Ph.D. degree in computer software and theory from Northwest University, Xi'an, China, in 2014.
He is currently a Lecturer with the Xi'an University of Technology. His research interests include application and security of CPS and modeling of effectiveness evalu...View more
Author image of Kan Wang
School of Computer Science and Engineering, Shaanxi Key Laboratory for Network Computing and Security Technology, Xi'an University of Technology, Xi'an, China
Kan Wang received the B.S. degree in broadcasting and television engineering from the Zhejiang University of Media and Communications, Hangzhou, China, in 2009, and the Ph.D. degree in military communications from the State Key Laboratory of ISN, Xidian University, Xi'an, China, in 2016.
From 2014 to 2015, he was with Carleton University, Ottawa, ON, Canada, as a Visiting Scholar funded by the China Scholarship Council. Si...Show More
Kan Wang received the B.S. degree in broadcasting and television engineering from the Zhejiang University of Media and Communications, Hangzhou, China, in 2009, and the Ph.D. degree in military communications from the State Key Laboratory of ISN, Xidian University, Xi'an, China, in 2016.
From 2014 to 2015, he was with Carleton University, Ottawa, ON, Canada, as a Visiting Scholar funded by the China Scholarship Council. Si...View more

I. Introduction

With the deep integration of smarter sensing components and more complex communication and control networks in the power system, it has changed prominently toward future power system, also called cyber-physical energy system (CPES), which is combined power networks and cyber-physical systems (CPS), as shown in Fig. 1. The supply and demand balance between power suppliers and customers is the fundamental driving force for the development of CPES. Therefore, accurate load forecasting, especially short-term load forecasting (STLF), will play a key role in CPES by providing power suppliers with data support for the coordination of supply and demand, as well as automatic electricity generation control in modern power systems [1], [2].

Author image of Dong Ding
School of Electrical Engineering, Xi'an University of Technology, Xi'an, China
Dong Ding received the B.S. degree in software engineering and the M.S. degree in computer system architecture from Xi'an University of Technology, Xi'an, China, in 2014 and 2018, respectively, where he is currently working toward the Ph.D. degree in electrical engineering.
Dong Ding received the B.S. degree in software engineering and the M.S. degree in computer system architecture from Xi'an University of Technology, Xi'an, China, in 2014 and 2018, respectively, where he is currently working toward the Ph.D. degree in electrical engineering.View more
Author image of Junhuai Li
School of Computer Science and Engineering, Shaanxi Key Laboratory for Network Computing and Security Technology, Xi'an University of Technology, Xi'an, China
Junhuai Li received the B.S. degree in electrical automation from the Shaanxi Institute of Mechanical Engineering, Xi'an, China, in 1992, the M.S. degree in computer application technology from the Xi'an University of Technology, Xi'an, in 1999, and the Ph.D. degree in computer software and theory from Northwest University, Xi'an, in 2002.
He is currently a Professor with the School of Computer Science and Engineering, Xi'an University of Technology, China. His research interests include the Internet of Things technology and network computing.
Junhuai Li received the B.S. degree in electrical automation from the Shaanxi Institute of Mechanical Engineering, Xi'an, China, in 1992, the M.S. degree in computer application technology from the Xi'an University of Technology, Xi'an, in 1999, and the Ph.D. degree in computer software and theory from Northwest University, Xi'an, in 2002.
He is currently a Professor with the School of Computer Science and Engineering, Xi'an University of Technology, China. His research interests include the Internet of Things technology and network computing.View more
Author image of Huaijun Wang
School of Computer Science and Engineering, Shaanxi Key Laboratory for Network Computing and Security Technology, Xi'an University of Technology, Xi'an, China
Huaijun Wang received the B.Sc. and M.Sc. degrees in computer science from the Xi'an University of Technology, Xi'an, China, in 2005 and 2010, respectively, and the Ph.D. degree in computer software and theory from Northwest University, Xi'an, China, in 2014.
He is currently a Lecturer with the Xi'an University of Technology. His research interests include application and security of CPS and modeling of effectiveness evaluation of security.
Huaijun Wang received the B.Sc. and M.Sc. degrees in computer science from the Xi'an University of Technology, Xi'an, China, in 2005 and 2010, respectively, and the Ph.D. degree in computer software and theory from Northwest University, Xi'an, China, in 2014.
He is currently a Lecturer with the Xi'an University of Technology. His research interests include application and security of CPS and modeling of effectiveness evaluation of security.View more
Author image of Kan Wang
School of Computer Science and Engineering, Shaanxi Key Laboratory for Network Computing and Security Technology, Xi'an University of Technology, Xi'an, China
Kan Wang received the B.S. degree in broadcasting and television engineering from the Zhejiang University of Media and Communications, Hangzhou, China, in 2009, and the Ph.D. degree in military communications from the State Key Laboratory of ISN, Xidian University, Xi'an, China, in 2016.
From 2014 to 2015, he was with Carleton University, Ottawa, ON, Canada, as a Visiting Scholar funded by the China Scholarship Council. Since 2017, he has been with the School of Computer Science and Engineering, Xi'an University of Technology, Xi'an. His current research interests include 5G cellular networks, resource management, and massive IoT.
Kan Wang received the B.S. degree in broadcasting and television engineering from the Zhejiang University of Media and Communications, Hangzhou, China, in 2009, and the Ph.D. degree in military communications from the State Key Laboratory of ISN, Xidian University, Xi'an, China, in 2016.
From 2014 to 2015, he was with Carleton University, Ottawa, ON, Canada, as a Visiting Scholar funded by the China Scholarship Council. Since 2017, he has been with the School of Computer Science and Engineering, Xi'an University of Technology, Xi'an. His current research interests include 5G cellular networks, resource management, and massive IoT.View more

References

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