Z
Zhao Yang Dong
Researcher at University of New South Wales
Publications - 930
Citations - 33916
Zhao Yang Dong is an academic researcher from University of New South Wales. The author has contributed to research in topics: Electric power system & Electricity market. The author has an hindex of 77, co-authored 872 publications receiving 23835 citations. Previous affiliations of Zhao Yang Dong include University of Newcastle & University of Queensland.
Papers
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Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network
TL;DR: The proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households and is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting.
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The 2015 Ukraine Blackout: Implications for False Data Injection Attacks
TL;DR: In this paper, the authors consider some implications for FDIAs arising from the late 2015 Ukraine Blackout event, and propose a false data injection attack (FDIA) framework.
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A Review of False Data Injection Attacks Against Modern Power Systems
TL;DR: A comprehensive review of state-of-the-art in FDIAs against modern power systems is given and some potential future research directions in this field are discussed.
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Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine
TL;DR: In this paper, an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation is proposed 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.
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Short-Term Residential Load Forecasting Based on Resident Behaviour Learning
TL;DR: In this article, a long short-term memory-based deep-learning forecasting framework with appliance consumption sequences is proposed to address the volatile problem in residential load forecasting, which can be notably improved by including appliance measurements in the training data.