## 10 Conclusions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline \multicolumn{1}{c}{**Research Gap**} & \multicolumn{1}{c}{**Future Direction**} \\ \hline Demand-side management & The integration of LF into consumer demand-side management plans has the potential to enhance the quality of the power system. Such integration may result in more economical power use, lower prices, and increased system reliability. \\ \hline Anomaly detection in LF & Anomaly detection in LF is underexplored, despite the complexities introduced by smart grids. The development of models capable of anticipating and detecting anomalies could enhance the accuracy of the results obtained. \\ \hline Evolutionary and metaheuristic optimization techniques & Combining evolutionary and metaheuristic optimization techniques can enhance the learning capabilities of ANNs, thereby improving the accuracy of their outputs. The hybridization strategy warrants investigation for better forecasting. \\ \hline Lack of STLF analysis in developing countries & The lack of STLF analysis in developing countries is due to data availability issues. It is of the utmost importance to improve data collection methods and to explore alternative data sources in order to achieve more accurate forecasting in these regions. \\ \hline Uncertainty quantification quantification & A research gap exists in the field of uncertainty quantification for STLF. Further methodologies should provide probabilistic forecasts and quantify uncertainty through scenario analysis, ensemble forecasting, and other appropriate techniques. \\ \hline STLF integration with smart technology & Integration of STLF with smart technology can enhance power systems, leading to smart grids, buildings, and microgrids. It improves energy efficiency, reduces costs, and enhances system reliability. \\ \hline Data imputation techniques & The gaps in STLF caused by missing data points must be researched and the implementation of effective data imputation techniques must be carried out. \\ \hline Transferability across regions & Assess the transferability of STLF models across different regions, considering variations in power system structure, climate, and load behaviors. \\ \hline Grid resilience considerations & Explore how STLF models can contribute to grid resilience by incorporating factors related to grid robustness and adaptability in the forecasting process. \\ \hline Privacy-preserving forecasting & Development of privacy-preserving techniques for STLF models is important, ensuring that sensitive data are protected while still allowing for accurate forecasting. \\ \hline \hline \end{tabular}
\end{table} 
Table 14: _Cont._forecasting. The necessity for models that can adapt to changes in characteristics over time is of paramount importance, as traditional models become obsolete in dynamic, real-time environments. A number of studies have been conducted on STLF models related to smart grids, smart buildings, and V2G applications. The development of energy storage systems based on second-life batteries could be facilitated by the use of accurate STLF, which would assist in maintaining the stability of the smart grid during periods of fluctuating renewable energy resources. In order to assist researchers in selecting the most appropriate models for load prediction, the performance accuracy of each study is presented in terms of MAE, RMSE, and MAPE values. The output of several fundamental time-series models, such as ARIMA and ARMA, indicates that more complex predictive models are necessary to enhance forecasting accuracy. The models must be capable of detecting sudden changes in the electric load data during peak hours. This study also examined a range of intelligent-computing-based approaches for STLF applications. Furthermore, it has been observed that neural network techniques have been utilized to varying degrees of efficacy in a multitude of electrical load forecasting research fields.