**6. Conclusions and Future Work**

In this paper, seven well-known machine learning algorithms were successfully applied to solar PV system data from Abha (Saudi Arabia) to predict the generated power. The prediction error of the algorithms was relatively low. This indicates that we can confidently evaluate the feasibility of installing solar PV systems in residential buildings using only a small set of weather station data. Although the algorithms behaved similarly, the Deep Learning technique gave the minimum error with the minimum set of selected features. However, Polynomial Regression produced the best prediction performance when we incorporated more features.

**Author Contributions:** Conceptualization, M.M., S.A. and A.S.S.; methodology, M.M. and S.A.; software, M.M.; validation, A.S.S., M.J.A. and S.B.; formal analysis, M.M. and S.A.; investigation, M.M.; resources, S.A. and A.E.A.; data curation, M.M. and A.E.A.; writing—original draft preparation, M.M.; writing—review and editing, M.M., A.S.S., S.A., M.J.A. and S.B.; visualization, M.M. and S.B.; supervision, S.A.; project administration, M.M., S.A. and A.S.S.; funding acquisition, S.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research is financially supported by the Deanship of Scientific Research at King Khalid University under research grant number (RGP1/207/42).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** This work would not have been possible without the financial support offered by King Khalid University. We would like to express our deepest gratitude to their generous support.

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