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Table 2:
Evaluation of the Regeneration Capability of the PV-RNN Model Trained with Different Values for w.
w'
1.00.50.250.150.10.01
ADS 343 229 103 17 
Mean of Variance 0.0007 0.0015 0.0039 0.005 0.021 0.1126 
w'
1.00.50.250.150.10.01
ADS 343 229 103 17 
Mean of Variance 0.0007 0.0015 0.0039 0.005 0.021 0.1126 

Notes: High meta-prior translates into deterministic dynamics, while low values produce random-process-like behavior. When w'=1.0, the divergence starts from the 343th step driven by low stochasticity, and as w' becomes smaller, the divergence starts earlier. When w'=0.01, the divergence starts immediately after the onset and the variance is high. The bold numbers show the model with the best regeneration capacity.

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