For the equivalent modeling of microgrid, LSTM-based equivalent model can present the correlation of operational state of microgrid in time domain and present the dynamic behaviors of the microgrid without considering the interaction of internal components.
Jan 13, 2020 · Abstract: Models of electrical equipment components are the basis of the transient stability studies of power system with multi-microgrids.
LSTM recurrent neural network is the state-of-the-art models for a variety machine learning problems, and the dynamic behaviors of microgrid under grid-connect ...
NASA/ADS · Microgrid Equivalent Modeling Based on Long Short-Term Memory Neural Network.
Sep 11, 2024 · This study proposes using a convolutional neural network (CNN) based on deep learning to address these challenges.
To build dynamic model when microgrid is a black-box system, a gated recurrent unit based neural network is proposed in this paper.
Sep 5, 2024 · This study includes approaches based on Long Short-Term Memory Neural Networks (LSTMs), with architectures ranging from Vanilla LSTM, Stacked ...
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Microgrid Load Forecasting Based on Improved Long Short‐Term Memory ...
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Mar 20, 2022 · In this paper, a load-forecasting algorithm for microgrid based on improved long short-term memory neural network (LSTM) is proposed. Firstly, ...
This paper mainly deals with the DC microgrid state estimation (SE) using a modified long short-term memory (LSTM) network.
Jul 20, 2024 · This approach is aimed at effectively extracting temporal data from energy datasets to improve the precision of microgrid behavior forecasts.