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Deep neural network and genetic algorithm synergistic optimization of new energy generation power combination prediction technology research

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Mar 19, 2025

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Figure 1.

Model of RBM
Model of RBM

Figure 2.

Genetic algorithm process
Genetic algorithm process

Figure 3.

Structure of the prediction model
Structure of the prediction model

Figure 4.

GA-DBN new energy power prediction model data acquisition results
GA-DBN new energy power prediction model data acquisition results

Figure 5.

Pearson correlation coefficient heat
Pearson correlation coefficient heat

Figure 6.

The photovoltaic processing data EMD handles partial results
The photovoltaic processing data EMD handles partial results

Figure 7.

Power curve of various kinds of weather photovoltaic power stations
Power curve of various kinds of weather photovoltaic power stations

Figure 8.

Electric dispatching
Electric dispatching

Figure 9.

Model prediction curve contrast
Model prediction curve contrast

Historical meteorological data of landscape hair field

Meteorological data category Numerical value
Annual average temperature/°C 10.165
Annual maximum temperature/°C 42.899
Annual minimum temperature/°C -25.563
Average annual relative humidity (%) 42.955
Annual average wind speed(m/s) 2.948
Maximum annual wind speed(m/s) 15.866
Annual average pressure/hPa 942.856
Annual average precipitation/mm 37.511
Maximum annual precipitation/mm 26.685

The prediction results of each model in the future 15min

Model eMAE eRMSE eMSE R2
LSTM 0.0378 0.0559 0.0752 0.8269
GRU 0.0359 0.0533 0.0821 0.8345
BiGRU 0.0285 0.0485 0.0715 0.8963
AM-BiGRU 0.0245 0.0405 0.0592 0.9254
TPA-BiGRU 0.0286 0.0398 0.0654 0.9345
CNN&TPA-BiGRU(EL) 0.0208 0.0349 0.0741 0.9425
DC-CNN&TPA-BiGRU(LR) 0.0215 0.0352 0.0685 0.9454
GA-DBN 0.0205 0.0342 0.0545 0.9525
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