**6. Conclusions**

This study proposes an idea for energy analysis based on a microgrid photovoltaic system using a deep learning method. The energy optimization of the microgrid was carried out using a photovoltaic-based energy system with distributed power generation. The data analysis has been carried out for feature analysis and classification using a Gaussian radial Boltzmann with Markov encoder model. When taking into account an MG with photovoltaics (PVs), solar radiations abruptly increase in intensity during the day. That will boost MG production at a specific moment. Table 2 presents the comparative analysis of the proposed method with EMS and LSTM and it was shown to achieve higher power analysis, energy efficiency, QoS, accuracy, precision, and recall for different circuit models. In power analysis, the proposed method achieves 5% and 2.4% increases compared to EMS and LSTM models, respectively, for resistance models. Similarly, it has shown an increase of 6% and 2.3% and 4.7% and 2.3% for reactance and admittance circuit models.

**Author Contributions:** Methodology, S.Q., M.M., P.R.K. and K.I.; Validation, P.C.; Formal analysis, S.Q.; Investigation, S.Q. and P.R.K.; Data curation, P.C.; Writing—original draft, M.M.; Writing—review & editing, K.I.; Visualization, M.M.; Supervision, M.M. and P.C.; Project administration, P.R.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not Applicable.

**Informed Consent Statement:** Not Applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.
