An improved LSTM-Seq2Seq-based forecasting method for electricity load
Frontiers in Energy Research, 2023 Power load forecasting has gained considerable research interest in recent years. The power load is vulnerable to randomness and uncertainty during power grid operations.
Yangyang Mu +4 more
doaj +1 more source
Wind power deviation charge reduction using long short term memory network
e-Prime: Advances in Electrical Engineering, Electronics and EnergyHigh penetration of variable generation like wind in modern power systems results in frequent load-generation imbalances. Additional spinning reserves and balancing services are deployed to reduce or avoid such imbalances.
Sandhya Kumari +3 more
doaj +1 more source
Long-Short-Term Memory Network Based Hybrid Model for Short-Term Electrical Load Forecasting
Information, 2018 Short-term electrical load forecasting is of great significance to the safe operation, efficient management, and reasonable scheduling of the power grid.
Liwen Xu +3 more
doaj +1 more source
Refining Short-Term Power Load Forecasting: An Optimized Model with Long Short-Term Memory Network
Journal of Computing and Information Technology, 2023 Short-term power load forecasting involves the stable operation and optimal scheduling of the power system. Accurate load forecasting can improve the safety and economy of the power grid.
Sile Hu +4 more
doaj +1 more source
Time-distanced gates in long short-term memory networks [PDF]
Medical Image Analysis, 2020 The Long Short-Term Memory (LSTM) network is widely used in modeling sequential observations in fields ranging from natural language processing to medical imaging. The LSTM has shown promise for interpreting computed tomography (CT) in lung screening protocols.
Riqiang Gao +11 more
openaire +2 more sources
Computer simulations of developmental change: The contributions of working memory capacity and long-term knowledge [PDF]
, 2008 Increasing working memory (WM) capacity is often cited as a major influence on children’s development and yet WM capacity is difficult to examine independently of long-term knowledge.
Pine, JM +5 more
core +1 more source
Long short‐term memory and convolutional neural network for abnormal driving behaviour recognition
IET Intelligent Transport Systems, 2020 Abnormal driving behaviours, such as rapid acceleration, emergency braking, and rapid lane changing, bring great uncertainty to traffic, and can easily lead to traffic accidents.
Shuo Jia +4 more
doaj +1 more source
An FPGA implementation of a long short-term memory neural network [PDF]
2016 International Conference on ReConFigurable Computing and FPGAs (ReConFig), 2016 Our work proposes a hardware architecture for a Long Short-Term Memory (LSTM) Neural Network, aiming to outperform software implementations, by exploiting its inherent parallelism. The main design decisions are presented, along with the proposed network architecture. A description of the main building blocks of the network is also presented.
João Canas Ferreira, Jose Fonseca
openaire +2 more sources
Accurate detection of sleep apnea with long short-term memory network based on RR interval signals [PDF]
, 2020 Sleep apnea is a common condition that is characterized by sleep-disordered breathing. Worldwide the number of apnea cases has increased and there has been a growing number of patients suffering from apnea complications.
Faust, Oliver +5 more
core +1 more source
IET Renewable Power GenerationWith the increasing penetration of grid‐scale wind energy systems, accurate wind power forecasting is critical to optimizing their integration into the power system, ensuring operational reliability, and enabling efficient system asset utilization ...
Mehmet Balci +3 more
doaj +1 more source