**5. Conclusions**

The exhaustible and inexhaustible sources of energy influence the economic growth of a country. The energy demand is a determinant of various functions, such as individual income, market structure, economic structure, lifestyle of individuals, and population change. The world may experience numerous challenges if there is uncertainty in energy supply in the near and far future. To determine the economic stability of a country, sustainable managemen<sup>t</sup> of energy is needed. Therefore, the prediction of energy demand is of utmost importance for the uniform allocation of available resources relating to industrial production, healthcare, agriculture, population, accessibility of water, education, and quality of life. By forecasting the demand, we can accommodate the power generated using the available storage facilities. Incorporating this feature in the power grid will help in maintaining a balance between all the power sources. Therefore, this paper has proposed the hybrid RNN-GBRT model for forecasting the load demand, validating its efficiency. In particular, the comparative analysis among all the considered forecasting models is presented based on three error indices to evaluate the performance of the proposed hybrid model for energy forecasting.

Another important issue addressed in this paper is the power theft that can affect the quality of the energy distribution service and cause economic losses. In most of the sectors of energy distribution, a medium to excessive rate of larceny and medium to low rate of detection exist despite numerous technologies. The intensity of theft differs among several parts of the country. However, the detection and punishment of illegal consumers are extremely challenging tasks. Tracing power theft at the root can prove invaluable to the government's power sector. The proposed algorithm for individual household consumption tracking will be extremely helpful in notifying anomalies both to the user, who is paying the extra amount in his/her power bill, and to the government, which is losing power and money.

Future works will propose techniques to address the problem of forecasting the energy demand in a network of householders in a smart district context, managing different users and detecting possible power theft or grid malfunctioning within the district.

**Author Contributions:** Conceptualization, methodology, formal analysis, and writing—original draft preparation, S.K.D., R.R.K., M.R.; writing—review and editing, S.K.D. and M.R.; visualization, M.P.F.; supervision, M.R., M.P.F. and A.M.M. 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:** Not applicable.

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