A review of graph neural networks and their applications in power systems
W Liao, B Bak-Jensen, JR Pillai… - Journal of Modern …, 2021 - ieeexplore.ieee.org
Deep neural networks have revolutionized many machine learning tasks in power systems,
ranging from pattern recognition to signal processing. The data in these tasks are typically …
ranging from pattern recognition to signal processing. The data in these tasks are typically …
Towards trustworthy energy disaggregation: A review of challenges, methods, and perspectives for non-intrusive load monitoring
Non-intrusive load monitoring (NILM) is the task of disaggregating the total power
consumption into its individual sub-components. Over the years, signal processing and …
consumption into its individual sub-components. Over the years, signal processing and …
基于人工智能技术的新型电力系统负荷预测研究综述
韩富佳， 王晓辉， 乔骥， 史梦洁， 蒲天骄 - 中国电机工程学报, 2023 - epjournal.csee.org.cn
在“双碳” 目标的驱动下, 构建以新能源为主体的新型电力系统是促进现代电力系统低碳转型发展
的重要前提与必然趋势. 由于复杂易变的多元负荷是新型电力系统的重要组成部分 …
的重要前提与必然趋势. 由于复杂易变的多元负荷是新型电力系统的重要组成部分 …
A deep learning method for short-term residential load forecasting in smart grid
Residential demand response is vital for the efficiency of power system. It has attracted
much attention from both academic and industry in recent years. Accurate short-term load …
much attention from both academic and industry in recent years. Accurate short-term load …
Electrical load-temperature CNN for residential load forecasting
M Imani - Energy, 2021 - Elsevier
Residential load forecasting is a challenging problem due to complex relations among the
hourly electrical load values along the time and also nonlinear relationships among the …
hourly electrical load values along the time and also nonlinear relationships among the …
Industrial power load forecasting method based on reinforcement learning and PSO-LSSVM
Q Ge, C Guo, H Jiang, Z Lu, G Yao… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
Influenced by many complex factors, it is very difficult to obtain high-performance industrial
power load forecasting. The industrial power load forecasting is deeply studied by fusing …
power load forecasting. The industrial power load forecasting is deeply studied by fusing …
A review of current methods and challenges of advanced deep learning-based non-intrusive load monitoring (NILM) in residential context
The rising demand for energy conservation in residential buildings has increased interest in
load monitoring techniques by exploiting energy consumption data. In recent years …
load monitoring techniques by exploiting energy consumption data. In recent years …
[HTML][HTML] Short-term load and price forecasting using artificial neural network with enhanced Markov chain for ISO New England
A Alhendi, AS Al-Sumaiti, M Marzband, R Kumar… - Energy Reports, 2023 - Elsevier
Nowadays, forecasting methods have gained significant attention, particularly with the
design and development of energy systems. In fact, accurate load and price forecasting is …
design and development of energy systems. In fact, accurate load and price forecasting is …
K-means and alternative clustering methods in modern power systems
As power systems evolve by integrating renewable energy sources, distributed generation,
and electric vehicles, the complexity of managing these systems increases. With the …
and electric vehicles, the complexity of managing these systems increases. With the …
A short-term residential load forecasting scheme based on the multiple correlation-temporal graph neural networks
Y Wang, L Rui, J Ma - Applied Soft Computing, 2023 - Elsevier
Accurate residential load forecasting (RLF) is of great significance for the decision-making
and operation of modern power system. In literature, deep neural network (DNN) based RLF …
and operation of modern power system. In literature, deep neural network (DNN) based RLF …