VMD-CNN-LSTM (unoptimized) and VMD-KOA-CNN-LSTM Comparison of the model prediction results with the actual power (Station site1).

VMD-CNN-LSTM (unoptimized) and VMD-KOA-CNN-LSTM Comparison of the model prediction results with the actual power (Station site1).

<p>VMD-CNN-LSTM (unoptimized) and VMD-KOA-CNN-LSTM Comparison of the model prediction results with the actual power (Station site1).</p>

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Bibliographic Details
Main Author: Jiangli Yu (4601986) (author)
Other Authors: Gaoyi Liang (22311429) (author), Lei Wang (6656) (author), Huiyuan He (22311432) (author), Yuxin Liu (505575) (author), Qi Liu (33068) (author), Xinjie Cui (498116) (author), Hao Wang (39217) (author)
Published: 2025
Subjects:
Biotechnology
Plant Biology
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Information Systems not elsewhere classified
mean absolute error
decomposed modal components
solid data foundation
reduce data complexity
photovoltaic power stations
photovoltaic power generation
term power prediction
traditional prediction model
lstm combined model
kepler optimization algorithm
kepler algorithm
prediction accuracy
data preprocessing
power system
power changes
lstm leverages
xlink ">
three work
subsequent predictions
study focuses
strongly proves
stable operation
significant optimization
result correction
reliable method
proposed method
innovatively introduce
innovative idea
grid connection
great significance
frequency characteristics
fluctuating problems
evaluation indicators
entire process
dynamic trends
deeply integrated
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