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Table 2:

Evaluation of the Regeneration Capability of the PV-RNN Model Trained with Different Values for

w

⁠.

	$w^{'}$
	1.0	0.5	0.25	0.15	0.1	0.01
ADS	343	229	103	17	4	1
Mean of Variance	0.0007	0.0015	0.0039	0.005	0.021	0.1126

	$w^{'}$
	1.0	0.5	0.25	0.15	0.1	0.01
ADS	343	229	103	17	4	1
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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