Prof. Zhaoyang Dong

Top Cited Scholar 2026

Nanyang Technological University, Singapore, Central Singapore, Singapore

Prof. Zhaoyang Dong

Nanyang Technological University, Singapore, Central Singapore, Singapore

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Academic Career Length

30Years (1997-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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18 September 2017 in IEEE Transactions on Smart Grid

Journal Article

Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

Youwei Jia

Yuan Zhang

As the power system is facing a transition towards a more intelligent, flexible and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network (RNN) based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.

22 November 2016 in IEEE Transactions on Power Systems

Journal Article

The 2015 Ukraine Blackout: Implications for False Data Injection Attacks

Gaoqi Liang

Steven R. Weller

Junhua Zhao

Fengji Luo

Zhaoyang Dong

In a false data injection attack (FDIA), an adversary stealthily compromises measurements from electricity grid sensors in a coordinated fashion, with a view to evading detection by the power system bad data detection module. A successful FDIA can cause the system operator to perform control actions that compromise either the physical or economic operation of the power system. In this letter, we consider some implications for FDIAs arising from the late 2015 Ukraine Blackout event.

22 March 2016 in IEEE Transactions on Smart Grid

Journal Article

A Review of False Data Injection Attacks Against Modern Power Systems

Gaoqi Liang

Junhua Zhao

Fengji Luo

Steven R. Weller

Zhaoyang Dong

With rapid advances in sensor, computer, and communication networks, modern power systems have become complicated cyber-physical systems. Assessing and enhancing cyber-physical system security is, therefore, of utmost importance for the future electricity grid. In a successful false data injection attack (FDIA), an attacker compromises measurements from grid sensors in such a way that undetected errors are introduced into estimates of state variables such as bus voltage angles and magnitudes. In evading detection by commonly employed residue-based bad data detection tests, FDIAs are capable of severely threatening power system security. Since the first published research on FDIAs in 2009, research into FDIA-based cyber-attacks has been extensive. This paper gives a comprehensive review of state-of-the-art in FDIAs against modern power systems. This paper first summarizes the theoretical basis of FDIAs, and then discusses both the physical and the economic impacts of a successful FDIA. This paper presents the basic defense strategies against FDIAs and discusses some potential future research directions in this field.

30 October 2019 in IEEE Transactions on Smart Grid

Journal Article

A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids

Ahmed S. Musleh

Guo Chen

Zhaoyang Dong

Cyber-physical attacks are the main substantial threats facing the utilization and development of the various smart grid technologies. Among these attacks, false data injection attack represents a main category with its widely varied types and impacts that have been extensively reported recently. In addressing this threat, several detection algorithms have been developed in the last few years. These were either model-based or data-driven algorithms. This paper provides an intensive summary of these algorithms by categorizing them and elaborating on the pros and cons of each category. The paper starts by introducing the various cyber-physical attacks along with the main reported incidents in history. The significance and the impacts of the false data injection attacks are then reported. The concluding remarks present the main criteria that should be considered in developing future detection algorithms for the false data injection attacks.

14 November 2013 in IEEE Transactions on Power Systems

Journal Article

Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine

Can Wan

Zhao Xu

Pierre Pinson

Zhaoyang Dong

Kit Po Wong

Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrap methods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified with the best performance. Consequently, a new method for prediction intervals formulation based on the ELM and the pairs bootstrap is developed. Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results demonstrate that the proposed method is effective for probabilistic forecasting of wind power generation with a high potential for practical applications in power systems.

27 March 2017 in IEEE Transactions on Power Systems

Journal Article

Short-Term Residential Load Forecasting Based on Resident Behaviour Learning

Yan Xu

Residential load forecasting has been playing an increasingly important role in modern smart grids. Due to the variability of residents' activities, individual residential loads are usually too volatile to forecast accurately. A long short-term memory-based deep-learning forecasting framework with appliance consumption sequences is proposed to address such volatile problem. It is shown that the forecasting accuracy can be notably improved by including appliance measurements in the training data. The effectiveness of the proposed method is validated through extensive comparison studies on a real-world dataset.

About Me

Prof. Zhao Yang Dong is Chair Professor and Head of the Department of Electrical Engineering at the City University of Hong Kong. His previous roles include Singapore Power Group Endowed Professor and Co-Director of SPG-NTU Joint Lab at Nanyang Technological University, Singapore; SHARP Professor of Energy System and Inaugrial Director of UNSW Digital Grid Futures Institute, the University of NSW, Australia; Director of ARC Research Hub for Integrated Energy Storage Systems; Head of School of Electrical & Information Engineering, The University of Sydney; and Ausgrid Chair Professor and Director of Ausgrid Centre for Intelligent Electricity Networks provided R&D support for the Smart Grid, Smart City national demonstration project of Australia. He also worked in industry as a manager for power system planning with the transmission network service provider of TAS in Australia. Prof. Dong’s research interests include power system planning and stability, smart grids/micro grids, smart cities, renewable energy systems, energy storage systems, and energy markets. He is a fellow of the IEEE and has held editorial roles for several IEEE Transactions and IET Journals.

Prof. Zhaoyang Dong

Prof. Zhaoyang Dong

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