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Artificial Intelligence-Based Ultra-Short-Term Power Load Forecasting

Applied and Computational Engineering
In the context of significant renewable energy integration, power load forecasting is viewed as an essential task in energy management and power system operation and scheduling. In an effort to enhance the accuracy and precision of power load prediction, a predictive technique based on Long Short-Term Memory (LSTM) networks enhanced by the quantum ...
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Deep Reinforcement Learning for Load Shedding Against Short-Term Voltage Instability in Large Power Systems

IEEE Transactions on Neural Networks and Learning Systems, 2023
Yonghong Luo, Chao Lu, Jennie Si
exaly  

Ultra-short term power load forecasting based on CEEMDAN-SE and LSTM neural network

Energy and Buildings, 2023
exaly  

Highly accurate peak and valley prediction short-term net load forecasting approach based on decomposition for power systems with high PV penetration

Applied Energy, 2023
Tingze Zhang, Xinan Zhang, Herbert Ho-Ching Iu   +2 more
exaly  

Short-Term Load Forecasting For Jordan's Power System

i-manager's Journal on Electrical Engineering, 2009
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Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

IEEE Transactions on Smart Grid, 2019
Weicong Kong, Zhao Yang Dong, Youwei Jia
exaly  

Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm

Applied Energy, 2020
Azim Heydari, Elmira Pirshayan, Davide Astiaso Garcia   +2 more
exaly  

Short term and medium term power distribution load forecasting by neural networks

Energy Conversion and Management, 2005
Tankut Yalcinoz
exaly  

Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory

Applied Energy, 2017
Yaoyao He, Xiaofen Lu
exaly  

Modeling and Forecasting Short-Term Power Load With Copula Model and Deep Belief Network

IEEE Transactions on Emerging Topics in Computational Intelligence, 2019
Tinghui Ouyang, Yusen He, Huajin Li
exaly