<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The paper takes the data of a 50 MW photovoltaic power generation system as a sample, divides the weather conditions into two categories according to whether there is a sudden change, optimises the decomposition number <italic>K</italic> and penalty factor of variational mode decomposition (VMD) by using the sparrow intelligent algorithm, decomposes the power sequence in a power mode by using the optimised VMD decomposition method and sends all sub-components to a long short-term memory (LSTM) network for prediction.</p>
</abstract>

The paper takes the data of a 50 MW photovoltaic power generation system as a sample, divides the weather conditions into two categories according to whether there is a sudden change, optimises the decomposition number K and penalty factor of variational mode decomposition (VMD) by using the sparrow intelligent algorithm, decomposes the power sequence in a power mode by using the optimised VMD decomposition method and sends all sub-components to a long short-term memory (LSTM) network for prediction.

Ultra-short-term power forecast of photovoltaic power station based on VMD–LSTM model optimised by SSA

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The paper takes the data of a 50 MW photovoltaic power generation system as a sample, divides the weather conditions into two categories according to whether...

Ultra-short-term power forecast of photovoltaic power station based on VMD–LSTM model optimised by SSA

Pubblicato online: 05 set 2022

Pagine: 823 - 834

Ricevuto: 25 apr 2022

Accettato: 15 giu 2022

DOI: https://doi.org/10.2478/amns.2021.2.00246

Parole chiave
photovoltaic power station, sparrow algorithm, long-term memory neural network, ultra-short term, power prediction

© 2023 Jing Yizhou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Ultra-short-term power forecast of photovoltaic power station based on VMD–LSTM model optimised by SSA

Pubblicato online: 05 set 2022

Pagine: 823 - 834

Ricevuto: 25 apr 2022

Accettato: 15 giu 2022

DOI: https://doi.org/10.2478/amns.2021.2.00246

Parole chiavephotovoltaic power station, sparrow algorithm, long-term memory neural network, ultra-short term, power prediction

© 2023 Jing Yizhou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
photovoltaic power station, sparrow algorithm, long-term memory neural network, ultra-short term, power prediction