**5. Case Studies**

To further analyze the MPPT practicability of TRL under PSC, it was compared with that of INC [11], GA [15], PSO [16], ABC [17], CSA [18], and TLBO [19], respectively. Four case studies are carried out in this section. Here, each meta-heuristic algorithm shares the same optimization cycle, which is chosen as 0.01 *s*. Meanwhile, the TRL parameters are given in Table 1.


**Table 1.** The parameters of TRL. TRL: Transfer Reinforcement Learning.

For MPPT under PSC, a buck–boost converter is employed, due to its advantages described in reference [33]. Table 2 demonstrates the parameters of the PV system. In addition, the rated values of environment temperature and solar irradiation are set as 25 ◦C and 1000 <sup>W</sup>/m2, respectively.


**Table 2.** The photovoltaic (PV) system parameters.
